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Staff Machine Learning Engineer - Ops

Negotiable

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Model Integration and Release Team Our Model Integration and Release team owns the engineering release process for Wayve’s driving models. When research and engineering teams develop new architectures, features, or data changes, we validate that they meet our quality, safety, and performance standards before they reach customers. The team works across ML engineering, AI Platform, evaluation, and CI/CD to protect the model baseline while continually improving how reliably and efficiently we deliver models.⁠ 🧠 Your day-to-day - Define and evolve Wayve’s model development and release processes, including branching strategies, release cadence, configuration management, and quality gates. - Review proposed model, architecture, code, and metric changes to ensure the implementation matches its intended outcome. - Assess evaluation results and determine whether releases meet Wayve’s quality and safety standards. - Identify delivery bottlenecks and work across teams to address their root causes. - Balance speed and rigour, making informed decisions about when to accelerate delivery and when a release needs further validation. 🧩 What you’ll be working on - Release pipelines covering the full ML training and delivery lifecycle. - Automated checks and tooling that identify issues earlier in development. - Reliable evaluation methods for assessing model changes and release readiness. - CI/CD workflows that streamline model integration and delivery. - Monitoring and observability that improve confidence in production releases. - Engineering standards and operational processes that enable teams to deliver high-quality ML systems at scale. 🙌 You should apply if - You have significant software integration and release experience, including branching strategies, release cadences, and configuration management. - You understand ML training, model lifecycles, and the infrastructure required to deliver models reliably. - You can review someone else’s model, architecture, or code changes and challenge decisions that do not align with their stated intent. - You have experience with ML tooling and technologies such as PyTorch, TensorRT, model registries, quantisation, or model deployment. - You are comfortable working with CI/CD systems and GitHub Actions. - You take ownership of ambiguous, cross-team problems and prefer fixing underlying processes rather than repeatedly patching individual releases. - You combine a strong quality mindset with the judgement to recognise when speed genuinely matters. - You communicate clearly and collaborate effectively across research, engineering, platform, evaluation, and product teams. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen: 30mins with Talent Partner. - Competency Interviews: 30mins with Hiring Manager. - Deep-dive technical interviews: ML Deep Dive, PyTorch Debugging and Tech Leadership; 3 hours total. - Final interview: Mission & Values Alignment, 1 hour. We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

W

Model Release Engineer

Negotiable

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Model Integration and Release Team Our Model Integration and Release team builds the platform that moves Wayve’s AI Driver models from promising new features through training, simulation, on-road testing and release. Working across the full model lifecycle, the team connects systems and teams to create a scalable, reliable and transparent path to production—helping Wayve deploy new models faster and with greater confidence. 🧠 Your day-to-day - Design and build Python services and microservices that orchestrate the end-to-end model-release workflow. - Integrate feature candidates into release branches and automate readiness checks before training begins. - Connect with training platforms and APIs to coordinate behavioural-cloning, reinforcement-learning and subsequent training steps. - Trigger simulation and on-road tests, collect results and surface quality gates, approvals and release status. - Productionise services on Azure and Kubernetes, improving availability, scalability, performance, monitoring and alerting. - Use AI coding agents to investigate issues, automate manual work and accelerate delivery. 🧩 What you’ll be working on - A trusted, end-to-end platform spanning feature integration, training, simulation, on-road testing, approvals and model promotion. - Distributed workflows and APIs connecting systems owned by Model Engineering, MLOps, Measurement, Evaluation, Simulation, Operations and Release Management. - Reliable approval gates, observability and operational tooling that provide clear visibility into model candidates and their progress. - Cloud-native services running on Azure and Kubernetes, with meaningful metrics, logging, tracing, monitoring and alerting. - Platform UI workflows, including React-based experiences where useful. - Agentic automation that reduces manual engineering effort and increases the team’s delivery capacity. 🙌 You should apply if - You have strong software or platform engineering experience building reliable production services or microservices, ideally in Python. - You have hands-on experience with Kubernetes, cloud-native infrastructure and production service operations; Azure experience would be an advantage. - You have strong system-design skills across distributed workflows, APIs, orchestration and multi-system integrations. - You have established observability using meaningful metrics, logging, tracing, monitoring and alerting. - You can collaborate effectively across organisational boundaries, agree dependable interfaces and work through conflicting priorities. - You use AI coding agents or LLM tools confidently and practically in your day-to-day engineering workflow. - Experience with MLOps, model productionisation, training pipelines, evaluation, simulation or model-release workflows would be an advantage. - Front-end development experience, particularly with React, would also be beneficial. This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency interviews (Python programming interview and hiring manager interview; 2 hours total) - Deep-dive technical interviews (Software operations/MLOps and system design; 2 hours total) - Final interview: values and overall fit (45 mins) We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

W

Data Scientist, Data Quality & Provenance Team

Negotiable

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 📊 About our Data Quality & Provenance Team Our Data Quality & Provenance team sits within Wayve’s AI Platform organisation and builds the evidence that helps teams make confident decisions about the data underpinning our embodied AI systems. We develop statistically robust methods, metrics and tools to determine when data-quality, annotation and evaluation signals can be trusted—and when they cannot. 🧠 Your day-to-day - Define measurable, statistically rigorous concepts of data quality, including coverage, label quality, uncertainty, provenance and model performance. - Apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage and black-box model evaluation. - Partner with annotation, autonomy and evaluation teams to translate practical quality questions into defensible metrics. - Build automated reports that clearly communicate confidence, limitations and appropriate interpretation. - Analyse large annotation datasets using Python and SQL to identify quality issues and inform decisions. - Iterate on metrics and reporting based on feedback from the teams using them. 🧩 What you’ll be working on - Statistical methods for distinguishing genuine data-quality problems from label ambiguity, inconsistent annotation or evaluation noise. - Labeler and dataset metrics that support training-data selection, simulation, annotation acceptance and model evaluation. - Reusable quality artefacts that replace one-off analyses with registered, automated metrics and reports. - Multi-rater and human-in-the-loop ML systems, including approaches such as Dawid–Skene-style models, psychometrics and inter-rater agreement. - Quality standards spanning coverage, uncertainty, provenance, annotation quality and predictive-model performance. - Integration of data-science outputs into the platform workflows used by engineering and research teams 🙌 You should apply if - You have strong foundations in classical statistics, including experimental design, sampling, modelling, estimation and inference. - You have practical experience with rater or annotator modelling, inter-rater agreement, label uncertainty or related methods. - You understand data quality across dimensions such as coverage, label quality, uncertainty, provenance and model performance. - You have worked with annotation tooling, QA methodologies, human-in-the-loop ML systems or multi-rater labelled datasets. - You have strong Python and SQL skills and are confident analysing large, complex datasets. - You can turn ambiguous stakeholder questions into rigorous, usable metrics and clearly communicate uncertainty, limitations and decision implications. - Experience with probabilistic or Bayesian modelling, production platform integrations or safety-critical data would be an advantage. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process Our process is clear and respectful of your time - Initial call / recruiter screen (30 mins) - Competency interview: Python Programming (60 mins) Data Curation Domain (60 mins) - Deep-dive interview: Hiring Manager Interview (60 mins) Systems design interview (60 mins) - Final interview: Mission and values alignment (45 mins) We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

W

Technical Specialist, ML Data

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Labeling Data Program Org owns the execution and creation of curated labeled datasets which are critical for training and evaluation of ML models that power the Waymo Driver. As a Technical Specialist in this team, you will be the operational backbone of our machine learning initiatives. You will own and drive the complex, cross-functional programs that deliver high-quality data—the lifeblood of our models. You will orchestrate the end-to-end data lifecycle, from defining requirements for new datasets and tooling to scaling data pipelines and ensuring our ML teams have the resources they need to innovate. This is a high-impact role for a technical, detail-oriented leader who thrives on turning ambiguous data needs into tangible, scalable solutions. You will: - Drive the ML Flywheel: Lead the end-to-end lifecycle of ML data, from initial mining and curation to labeling policy definition, validation, and model evaluation. Work cross-functionally to ensure coordination and alignment on objectives and key results. - Translate Policy to Code: Lead the development of sophisticated labeling policies for complex AV domains (e.g., behavior prediction, long-tail edge cases). Convert ambiguous ML quality problems into precise, scalable annotation policies and data taxonomies. - Build Evals & Metrics: Design and implement ML evaluation frameworks. Identify key data-centric drivers of model performance and create the metrics that track ML quality at the data level. - Cross-Functional Leadership : Communicate effectively with technical and non-technical audiences at various levels of seniority, including producing analytical write-ups, dashboards, and data visualizations to convey your findings and recommendations to our team and cross-functional stakeholders - Influence ML data selection strategies (active learning, hard-mining) to ensure we are labeling the most impactful data to maximize ROI from the labeling effort You have: - 8+ years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data. - Deep understanding of the ML data lifecycle: labeling, taxonomy design, quality control, and data curation. - Experience with ML Data Flywheel and working understanding of ML development life cycle (e.g., model deployment,model evaluation, data processing, debugging, fine tuning). - Background in leading and managing complex programs that span across organizations and functions, with specific experience in Machine Learning data annotation or Human-in-the-Loop initiatives. - Strong ability to thrive in a dynamic environment, demonstrating comfort and effectiveness when dealing with ambiguity. - Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations. We prefer: - Experience with scripting language, machine learning tools, techniques and systems (including prompt engineering and fine-tuning LLMs ) is a strong plus. - Demonstrated ability to extract, manipulate, and apply machine learning techniques to high volumes of critical, product-related data. - Demonstrated ability in working with a variety of engineering stakeholders to gather requirements, explain models, and iterate to make improvements. - Excellent written and verbal communication and ability to describe technical implementations or analyses to a non-tech audience in an effective manner. - Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment. - A greater focus on using your subject matter expertise for results analysis and direct customer consultation in the development of new and improved. solutions. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $159,000 - $202,000 USD

W

Staff Software Quality Safety Operations Specialis...

$190,000 – $234,000

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Software Quality Operations (SWQOps) team is at the heart of ensuring the safety, reliability, and quality of the Waymo Driver. Our mission is to build an adaptable and scalable operation, increasingly powered by AI, to deliver the crucial insights necessary to confidently deploy and grow Waymo's autonomous vehicle service. Why This Team is Essential to Waymo's Success: Waymo is undergoing unprecedented growth, rapidly expanding into new cities (targeting ~20 new cities by EOY 2026) and launching new vehicle platforms. SWQ Ops plays a critical role in this expansion, making it possible to scale safely and efficiently. The Safety Operations team within SWQ Ops owns the scale delivery of datasets and methodologies used to evaluate the Safety and performance of the driver as Waymo continues to scale. We are on the front lines of: - De-risking New Deployments: Through meticulous triage of driving and simulated events, issue discovery, and continuous field monitoring, SWQOps provides early warnings and critical insights. This "early intervention in RO issue detection" ensures operational resilience and safety, particularly in new and complex environments, which is critical as Waymo enters multiple new cities and ramps up platforms like Ojai. - Driving Engineering Velocity: By handling the vital work of performance evaluation, issue deep-dives, and data set curation, SWQOps collaborates heavily and allows Waymo's Engineering, SysEng, Simulation, and Data Science teams to focus on their core tasks of developing and improving the Waymo Driver. - Enabling Market Expansion: Our team is deeply integrated into every stage of Waymo's market entry framework, from initial city evaluation (OK2Plan) to scaling operations (OK2Scale). We provide the necessary data analysis, policy development, and quality assurance to unblock critical milestones, preventing slowdowns in market expansion velocity. - Data Flywheel: Supporting the development of a single, automated, end-to-end machine learning flywheel for the entire Waymo Driver. A successful flywheel will be the core engine for scaling our technology, enabling faster ODD expansion, quicker remediation of driving issues, and a significant reduction in the engineering effort required to maintain and improve the driver. You will: - Drive the strategy and technical implementation of decomposing complex safety triage workflows. Develop and apply advanced operational and ML techniques to enable automation, ensuring scalability as mileage and geographic operations expand. - Serve as the subject matter expert for human-in-the-loop ML systems in the safety problem space. Define and refine requirements for generating high-quality, dense, and broad human feedback to optimize ML models performance. - Partner with Engineering to design, test, and deploy cutting-edge Machine Learning (ML) and Generative AI (Gen-AI) models and tools to drive step-change improvements in issue discovery & detection, triage efficiency, and quality assurance. - Leverage AI-powered insights and traditional triage signals to proactively identify emerging on-road issue trends, new risk scenarios, and edge cases. Develop and refine data-driven strategies for issue discovery and monitoring, enhanced by ML model outputs. - Serve as the key link between AI/ML development and operational execution. Author and drive the adoption of foundational technical policies and standards, strategic roadmaps, and process blueprints that enable the organization to scale and support stakeholder needs. - Advise senior stakeholders on the long-term technical strategy and operational capabilities of the organization, serving as a trusted partner for critical decisions. You have: - BS/BA degree or 7+ years of relevant work experience in AV Software Quality Operations / ML Operations - Proven ability to manage complex, technical projects and experience working across technical partners (Product, Engineering, Data Science, Systems Engineering) to drive outcomes. - Increased competency in supporting all phases of the machine learning development lifecycle, from data preparation and training to validation, deployment, and continuous monitoring. - Experience with ML testing and validation, including dataset quality assurance, bias detection, edge-case scenario testing, and performance evaluation using statistical metrics. - Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations. - A proven ability to work in a fast-paced, high-stress environment while maintaining good judgment - Excellent communication and interpersonal skills to effectively collaborate with a wide range of individuals in a diverse and dynamic work environment. We prefer: - Undergraduate in technical degree preferred. - Experience within A / V space. - Competency in LLM / transformer models, and / or ML for robotics domain experience. - Competency in SQL querying / data analysis. - Experience working with offshore teams / multiple local operations hubs. Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $190,000 - $234,000 USD

W

Staff Machine Learning Engineer, Simulation

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Driver Understanding and Evaluation team at Waymo develops a rich understanding of Waymo Driver's behavior. With over 1 million driverless miles per week, it is critical that Waymo can understand and assess the behavior of all its vehicles - both in the field and in simulation - with automated algorithms. The learned metrics team is a strategic bet to use machine learning to ensure we can scale to meet Waymo's goals. We collaborate across teams to bring ML to production systems and build what is Waymo's reward function. We build and operate large-scale machine learning and data systems, simulation workflows, and insight tools. We combine expert human judgements and advanced machine learning models to deliver training and evaluation data for the Waymo driver. We are looking for researchers and software engineers who are passionate about developing production grade machine learning systems for our autonomous vehicles and have an incessant drive to improve the performance of our technology stack. In this hybrid role, you will report to an Engineering Manager You will: - Report into the TLM for the Learned Metrics Team - Develop ML models that assess our autonomous vehicle's behavior. - Develop ML infrastructure to support performant models. - Collaborate across teams to bring state-of-the-art to production. You have: - BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area, or equivalent work experience - 5+ years of experience building productionized ML models - Code and design skills: comfort building production systems (Python / C++) - Background in applied Deep Learning - A track record in improving model quality We prefer: - 8+ years of experience building productionized ML models - Experience in reinforcement learning, transfer learning, or learning. - Experience with large scale data and models The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000 - $310,000 USD

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Senior Software Engineer, Eval Efficiency & Optimi...

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. As a Software Engineer, you will solve ill-defined problems at the intersection of statistics, machine learning, and scalable systems engineering. You will develop creative quantitative models to address eval challenges, while providing rigorous mathematical and experimental proof of the correctness and statistical efficiency of your solutions. You will: - Tackle ill-defined, ambiguous problems and reduce complex systems into tractable quantitative models. - Develop statistical techniques that enable our evaluation pipelines to deliver better signals with fewer resources. - Design and build automation to enhance the efficiency and scalability of our evaluation infrastructure. - Collaborate with other engineers, data scientists, product managers, and the leadership team to deliver evaluation products and help make data driven decisions. - Champion code health and best practices in a large and complex code base. You have: - BS in Math, Physics, or another quantitative field - Fluency in implementing statistical, ML, and general mathematical algorithms for large datasets - Experience adapting techniques in research papers to applied problems - Strong self-motivation and comfort with ambiguity to navigate complex systems and pursue open-ended problems to completion - 3-5 years of experience with - Navigating and modifying a large code base containing a variety of languages, such as C++, Python, and SQL - Performing statistical analyses - Building data processing pipelines - Writing, reviewing, and merging code following industry standards for code health and maintainability We prefer: - PhD in a quantitative field - Experience programming in C++ and Python - Experience developing, evaluating, and deploying products at scale - Experience with Agentic AI The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD

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Senior Software Engineer - ML/ Fraud Prevention

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. You will: - Train, evaluate, and ship production machine learning models to score rider risk in real time, directly protecting revenue from payment fraud and reducing vehicle vandalism. - Engineer and maintain high-throughput, low-latency feature pipelines over payment, trip, device, and identity events, ensuring consistency between offline training and online serving. - Build and operate the real-time model serving infrastructure integrated into the critical trip creation and payment authorization paths under strict latency and availability budgets. - Design and implement safe, graduated enforcement mechanisms with full audit trails, reversibility, and support for rider appeals. - Partner cross-functionally with Product, Finance, and Safety to balance revenue protection, rider trust, and compliance as Waymo scales to new markets. You have: - Bachelor's degree in Computer Science, Mathematics, Statistics, or equivalent practical experience. - 5+ years of software engineering experience building distributed backend systems or production ML systems (Python, Java, Kotlin, C++, Go, or similar). - Hands-on experience training, evaluating, and deploying machine learning models to production. - Experience serving models or critical business logic in low-latency, high-availability production environments. - Strong fundamentals in data modeling, and SQL analytics. We prefer: - Experience in fraud prevention, risk management, trust & safety, payments, or another adversarial domain where attackers actively adapt. - Experience with graph/entity-clustering techniques, rules engines, and anomaly detection alongside traditional ML. - Proven track record building feature/data pipelines and maintaining consistency between training data and real-time inference. - Experience serving models or critical business logic in low-latency, high-availability production environments. Disclosure for WA Based & Remote Roles: In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: Health, dental, vision, life, disability insuranceRetirement Benefits: 401(k) with company matchPaid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employmentSick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year US Remote Only: Please note that Waymo may not be able to employ remotely in all US locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process.

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Senior Data Scientist

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design. In this hybrid role you will report to a data science manager. You will: - Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation. - Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving. - Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc. - Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods. - Derive data-driven conclusions and communicate findings to senior stakeholders. - Establish yourself as the point-of-contact for a significant project area by using data to drive technical decisions and demonstrate success. - Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products. - Mentor other data scientists and provide constructive technical feedback within the team and across Waymo. You have: - Degree in a quantitative field (e.g. Statistics, Mathematics, Physics) - 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience - Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models - Demonstrated knowledge of Python/SQL/R data analysis libraries and packages We prefer: - PhD in a quantitative field - A demonstrated track record of independently driving data science projects to deliver business value - Experience solving problems related to Autonomous Driving or Ride Hailing - Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $204,000 - $259,000 USD

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Policy & Quality Specialist- ML Perception Data

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. As an L4 Policy & Quality Specialist, you will serve as the Mountain View operational backbone of the Labeling Policy Program. You will play a key role in accelerating MTV Perception Engineering velocity by translating complex machine learning data requirements into consistent, high-quality, and scalable labeling policies. Operating in the same time zone as our core engineering partners, you will drive rapid policy iteration, create critical golden datasets to enable fast labeling queue setup. This is an execution-focused role for a technical, detail-oriented specialist who thrives on driving clarity, alignment, and operational excellence in labeling workflows. You will: - Translate requirements to policies: Collaborate directly with MTV-based Perception Engineers/ ML model owners to understand their specific data goals, translate their ambiguous machine learning requirements into precise labeling instructions, and publish clear, actionable labeling policies (with support from the HYD Policy Specialist and vendor partners). - Drive queue readiness & golden datasets: Speed up the initial labeling queue setup process by executing rapid policy iterations and hand-crafting golden datasets (small-scale baseline datasets of 10s of examples) to establish quality baselines before launching full-scale operations. - Direct vendor teams: Provide technical guidance and operational direction to vendor labeling experts to enable rapid policy setup and ensure that the ~10 active labeling queues under your purview run smoothly and meet safety and performance objectives. - Address edge cases & regional nuances: Provide critical, detailed inputs on long-tail edge cases and coordinate with regional country specialists to ensure country-specific driving rules and local nuances are accurately captured and validated, ahead of Waymo's deployment in these new countries. - Enable quality and process improvements: Monitor labeling pipelines, conduct targeted technical analyses to identify data quality trends, and build/maintain automated data analysis tools to proactively identify improvements in the broader labeling workflow. - Facilitate cross-functional knowledge sharing: Act as the primary technical interface between requesters and operations, ensuring on-ground dissipation of policies, managing policy amendments, and resolving complex escalations from requestors or vendor teams. You have: - 4-5+ years of experience in data analysis, operations, or program management with a focus on machine learning data annotation, taxonomy design, or human-in-the-loop workflows. - Operational project management: Demonstrated ability to work independently on operational workflows and successfully project manage small sub-working groups or vendor squads. - Core ML data lifecycle understanding: Practical knowledge of dataset curation, labeling pipelines, data quality control metrics, and baseline model evaluation concepts. - Analytical aptitude: Experience conducting technical data analyses using pre-established tools (or building simple automation scripts) to diagnose pipeline issues, track vendor quality, and generate actionable insights. - Adaptable & detail-oriented: Comfort working within a dynamic environment, translating vague technical needs into clear documentation, and maintaining a high standard of attention to detail. We prefer: - Experience with scripting languages (e.g., Python, SQL) or basic automation techniques to parse high volumes of critical data. - Experience collaborating with software engineering stakeholders to gather structured requirements and explain technical operational policies. - Prior experience working across multiple geographic locations and managing vendor-hosted operations. - Familiarity with prompt engineering and evaluation of model outputs (AI-generated workflows) is a plus. The expected base salary range for this full-time position is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $130,000 - $160,000 USD

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Group Product Manager, DevAI & Agentic Workflows

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Waymo’s Product Management Team is a mission-driven team responsible for writing and enacting first-of-their-kind playbooks to bring fully autonomous driving technology to market. Our team develops simple solutions for complex problems by orchestrating cross-functional efforts to safely drive Waymo’s technology and the products it enables forward. We do this by understanding the customer, the business, and the technology. We are humble about the scope of our work, collaborative in our approach to problem-solving, and ambitious about our vision for the future. This role follows a hybrid work schedule and reports to the Director of Product Management. You will: - Define a compelling vision for Waymo's DevAI platform, shaping how autonomous driving software is built by completely transforming internal workflows into AI-first paradigms. - Exercise a highly focused and disciplined mindset to ruthlessly prioritize investments. You will guide a high-impact core team in a rapidly changing technological landscape. - Act as the primary thought leader and educator on AI-Powered Devtools for all of Waymo's engineers—cutting through the hype, organizing fragmented information (newsletters, new tools), and keeping teams focused on actionable insights. - Own the developer productivity survey and engineering education programs, ensuring they are tightly aligned with and accelerating our DevAI strategy. - Resolve technical and strategic ambiguities, making highly complex product decisions that carry long-term strategic impact for all Waymo engineers. - Set up the organization to grow by simplifying interfaces and processes for AI-Powered Devtools adoption from inception to deployment. - Establish robust, data-driven frameworks (e.g., DX Framework, SPACE metrics) to measure the true ROI of DevAI integration. - Partner with engineering and security teams to design and implement secure integration layers, ensuring AI tools safely access proprietary data without exposing core assets. You have: - Deep engineering experience coupled with hands-on, practitioner-level expertise using modern coding agents and agentic development framework. - Extensive experience as a Product Manager, with a track record of building and launching complex developer tools, platforms, or AI-integrated products. - Demonstrated AI fluency (Generative AI, LLMs) and a deep understanding of how they map to and transform real-world developer workflows. - An entrepreneurial mindset with the ability to navigate high ambiguity and pioneer new paradigms. - Strong cross-org leadership skills, uniquely capable of identifying collisions, mediating solutions, and inspiring both engineering and research teams. We prefer: - Experience driving organizational mindset shifts and large-scale, high-visibility technical initiatives that impact entire engineering organizations. - Prior hands-on experience building, implementing, or managing tools utilizing the Model Context Protocol (MCP) or similar agent-to-application integration frameworks. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $241,000 - $297,000 USD

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Data Scientist

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design. In this hybrid role you will report to a Data Science Manager. You will: - Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation. - Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving. - Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc. - Frame and solve ambiguous problems, derive data-driven conclusions, and communicate findings to senior stakeholders. - Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products. You have: - Degree in a quantitative field (e.g. Statistics, Mathematics, Physics) - 3+ years of industry experience solving data science problems or a PhD in a quantitative field - Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models - Demonstrated knowledge of Python/SQL/R data analysis libraries and packages We prefer: - PhD in a quantitative field - Experience solving problems related to Autonomous Driving or Ride Hailing - Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $170,000 - $216,000 USD

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2027 Summer Intern, MS/PhD, Perception, Machine Le...

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the self-driving car, i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. Waymo interns work with leaders in the industry on projects that deliver significant impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skillset. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! This internship will be based on-site at our headquarters in Mountain View, CA. You will: - Design and implement state-of-the-art multi-modality (camera/radar/lidar/audio) and multi-task perception models. Potential project areas include: - 3D object detection and tracking, - Open vocabulary detection applying world knowledge models, - 3D occupancy detection, - Semantic segmentation, - Large-scale foundation models, - Pre-training and self-supervised learning through forecasting. - Train and evaluate ML models on our vast and high fidelity data from Waymo driving logs. - Gain experience using large scale ML infrastructure and working in a team environment. You have: - Enrolled in a PhD program in Computer Science, Robotics, or a similar technical field of study or enrolled in a master's program with a strong publication record. - Experience in Python - Experience building ML models with JAX, PyTorch or Tensorflow We prefer: - Strong track record of high quality ML / CV research. Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI. - Experience in object detection, segmentation, multiple-object tracking audio perception or occupancy detection. Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly Masters Pay $70 - $70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 - $85 USD

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Junior/Senior/Staff Software Engineer, Inference /...

Negotiable

About the Role We're looking for a Software Engineer to build the systems that treat infrastructure as software. This role owns the software state machines that provision hardware, bring it into service, and manage its full lifecycle — turning racks of GPUs into running inference clusters without a human touching a runbook. The Research and Inference team is your customer: today they file tickets and wait; the target state is that they issue a single API call to stand up, scale, or tear down a cluster, and the system takes care of the rest. The platform is manifest-driven such that teams declare the desired state of a cluster or host — shape, topology, software stack — and the system is responsible for reconciling reality to that manifest, continuously, through every stage of its lifecycle. You will design the engines that manifest the schema, the engines that execute against it, and the workflows that carry a piece of hardware or a cluster from one state to the next—taking it from bare metal to a fully functioning AI cluster for training or inference. You'll write production code which is typed, tested, versioned, and deployed through CI/CD that models infrastructure state and reconciles it, the same way a Kubernetes controller reconciles a cluster's desired state. Success looks like eliminating manual provisioning work, not documenting it better. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities - Build the provisioning state machine: design and implement the software that models the full lifecycle of a physical host from discovery, inference bring-up to GPU driver/CUDA stack, health validation, and decommission/RMA — as explicit, versioned states and transitions. - Build the self-service API: design declarative APIs and a control plane so the inference team can request, scale, and tear down inference clusters with one API call — no ticket, no human in the loop. - Automate self-healing: detect degraded or failed nodes, drain them safely, trigger repair or replacement, and reintroduce healthy capacity into the pool automatically. - Own reliability of the pipeline: idempotency, retries, rollback, and drift detection so the provisioning system is as dependable as any other production service. - Partner with the inference/ML platform team: understand the cluster shapes they need — topology, interconnect, scheduling constraints — and encode them as first-class abstractions in the platform. - Engineer it like software: strong typing, automated tests, code review, versioning, and CI/CD for infrastructure code — this is a product, not a collection of Ansible playbooks. Requirements Core requirements (all levels): - Strong software engineering background in Go, Python, Rust, or similar — you write and test real software for a living. - Experience with durable workflow orchestration tools such as Temporal, Cadence, or equivalent to run long-lived, manifest-driven workflows that survive failures and resume mid-execution. - Experience building software control planes or orchestration systems that model state and reconcile it over time (e.g., Kubernetes controllers/operators, custom reconciliation loops, workflow engines). - Experience with event-driven systems — designing and building software around message queues, event streams, or pub/sub (e.g., Kafka, NATS, SQS) rather than polling or cron-driven scripts. - A product mindset. You’ve built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. Nice to have: - Exposure to bare-metal provisioning (PXE/iPXE, Redfish/IPMI, BMC) and/or networking fundamentals (VLANs, BGP, fabric design), or GPU/accelerator infrastructure. - Experience with GPU cluster software stacks (NCCL, CUDA, InfiniBand/RoCE). - Prior work at a hyperscaler, GPU cloud, or datacenter-scale infrastructure organization. - Systems programming in Rust or Go. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

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Junior/Senior or Staff Software Engineer, Inferenc...

Negotiable

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities - Build the provisioning state machine: design and implement the software that models the full lifecycle of a physical host from discovery, inference bring-up to GPU driver/CUDA stack, health validation, and decommission/RMA — as explicit, versioned states and transitions. - Build the self-service API: design declarative APIs and a control plane so the inference team can request, scale, and tear down inference clusters with one API call — no ticket, no human in the loop. - Automate self-healing: detect degraded or failed nodes, drain them safely, trigger repair or replacement, and reintroduce healthy capacity into the pool automatically. - Own reliability of the pipeline: idempotency, retries, rollback, and drift detection so the provisioning system is as dependable as any other production service. - Partner with the inference/ML platform team: understand the cluster shapes they need — topology, interconnect, scheduling constraints — and encode them as first-class abstractions in the platform. - Engineer it like software: strong typing, automated tests, code review, versioning, and CI/CD for infrastructure code — this is a product, not a collection of Ansible playbooks. Requirements Core requirements (all levels): - Strong software engineering background in Go, Python, Rust, or similar — you write and test real software for a living. - Experience with durable workflow orchestration tools such as Temporal, Cadence, or equivalent to run long-lived, manifest-driven workflows that survive failures and resume mid-execution. - Experience building software control planes or orchestration systems that model state and reconcile it over time (e.g., Kubernetes controllers/operators, custom reconciliation loops, workflow engines). - Experience with event-driven systems — designing and building software around message queues, event streams, or pub/sub (e.g., Kafka, NATS, SQS) rather than polling or cron-driven scripts. - A product mindset. You’ve built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. Nice to have: - Exposure to bare-metal provisioning (PXE/iPXE, Redfish/IPMI, BMC) and/or networking fundamentals (VLANs, BGP, fabric design), or GPU/accelerator infrastructure. - Experience with GPU cluster software stacks (NCCL, CUDA, InfiniBand/RoCE). - Prior work at a hyperscaler, GPU cloud, or datacenter-scale infrastructure organization. - Systems programming in Rust or Go. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

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Software Engineer, Supercomputing

Negotiable

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an engineer to design, build, and operate the GPU supercomputing environment that powers large‑scale training and inference. You will deliver high‑performant, reliable, and cost‑efficient compute so our users and researchers can move fast at scale. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Operate and automate large GPU clusters including provisioning, imaging, and capacity planning. - Write software that abstracts cluster management and presents a unified interface for training and inference. - Extend scheduling/orchestration (Kubernetes, Slurm, or similar) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy. - Monitor and improve operational metrics of speed, reliability, and error recovery. - Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage. - Partner with researchers to unblock scale runs and advise on parallelism and performance trade‑offs. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, engineering, or similar. - Proficiency in at least one backend language (we use Python or Rust). - Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm). - Comfort operating across the stack and owning projects end-to-end. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Strong systems background: Linux, networking, and infrastructure‑as‑code. - Familiarity with CUDA/NCCL and performance profiling for distributed training/inference. - Prior work supporting large‑scale model training or inference environments. - Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures. - Track record of working in fast-paced environments balancing care with urgency. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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Senior Data Scientist I, Real World Evidence (RWE)...

Negotiable

Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. The Real World Evidence (RWE) group within the Life Sciences R&D team at Tempus works with major pharmaceutical partners to provide best-in-class data, analysis, and methodological guidance for Tempus’s real-world data offering. We are seeking a highly motivated and solutions-oriented RWE Sr. Data Scientist I with experience and interest in oncology and epidemiological study design to join our team. This role requires the ability to lead observational studies, derive insights from complex real-world clinical data, implement advanced statistical methods, and leverage cutting-edge AI tools to scale tasks and augment insights. Key Responsibilities Pharma Collaboration & Strategy: Lead the design and execute delivery of RWE analyses for key pharma clients. You will be responsible for translating complex drug development questions and into actionable research plans that use Tempus data for trial design and outcomes research. Real World Data Expertise: Lead the derivation of complex real-world endpoints using extensive coding, demonstrating deep comprehension of Tempus clinical and molecular data structures and complexity, while also serving as an expert on the methodological nuances and limitations of real-world data. Methodology & Platform Contribution: Build technical standards for the team, staying up-to-date on methodological advancements in real-world studies (e.g., causal inference, survival analysis) and oncology guidelines (NCCN and ongoing clinical trials). Contribute to reusable code and internal packages that can be applied across multiple collaborations. AI & LLM Innovation: Incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation. Scientific Interpretation & Communication: Interpret results of RWE analyses to draw appropriate inferences based on study design/statistical methods, while also evaluating study limitations. Communicate complex methods and results clearly to both technical and non-technical stakeholders. Prepare and present internal reports, external-facing deliverables, and, where appropriate, manuscripts or conference materials. Cross-Functional Collaboration: Collaborate with internal product, oncology, and clinical abstraction, and real-world data science teams to continually enhance Tempus data quality, products, and analytical best practice. You will proactively identify gaps in current products and ensure that customer feedback is represented in development of new products. Oncology & RWE Domain Expertise: Grow and maintain deep expertise in oncology clinical guidelines (e.g., NCCN) and emerging RWE methodologies. Minimum Qualifications Education: Education in epidemiology, biostatistics, data science, public health, or a related field, to the level of either: PhD and 2+ years of additional work experience Master’s degree and 4+ years of additional work experience Technical and Statistical Proficiency Expert-level proficiency with observational real-world healthcare data, including analytical experience with time-to-event methodologies (survival analysis). Proven expertise in executing RWD analytical studies. Proficient in using R and SQL, especially statistical tools and packages. Proficiency applying machine learning, LLM-based coding assistants (e.g., Claude Code, Copilot, Cursor) and agentic frameworks to support data analysis, code review, or scientific documentation workflows. Adherence to good software engineering practices (version control, modular code, documentation). Experience with code review. Communication & Client Focus: Demonstrated experience interfacing with clients, showcasing adeptness in presenting and tailoring messaging to a variety of stakeholders. Soft Skills: Excellent written and verbal communication skills with strong project management skills. Ability to thrive in a fast-paced, dynamic environment working with multi-disciplinary scientists on complex problems. Preferred Skillsets Experience working with Pharma or drug development. Experience in clinical trial design (particularly Phase II-III) in the clinical development space. Analytical proficiency with claims, EHR, or registry data. Practical experience building, fine-tuning, or configuring LLM-based tools and agentic workflows specifically for scientific discovery. Knowledge of oncology guidelines (e.g., NCCN). Significant experience analyzing biomarker, genomic, or other high-dimensional molecular data alongside clinical datasets. Experience with cloud platforms such as AWS and/or BigQuery and/or Google Cloud Platform (GCP). #LI-DA1 CHI: $120,000-$170,000 NYC/SF: $130,000-$180,000 The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. Additionally, for remote roles open to individuals in unincorporated Los Angeles – including remote roles- Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Senior Data Engineer

Negotiable

Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. We are building the Patient Evaluation Engine: a high-scale, multi-modal healthcare platform where autonomous AI agents reason over clinical data to drive real-time clinical evaluation across federated networks of hospitals. We are looking for a Senior Data Engineer to build and own the data platform underneath it — the pipelines, models, and services that make EHR records, genomic results, and cardiovascular imaging discoverable, trustworthy, and usable by agents. This is a data engineering role at its core, and it asks for two things beyond the usual scope. First, you should be a capable software engineer: the person who builds the pipeline here is the person who writes the service that exposes it, and you will regularly work in our TypeScript application and service code rather than handing that off. Second, you should know cloud infrastructure well, specifically Google Cloud — you will make real decisions about how this platform is deployed, scaled, secured, and paid for, not just what runs on it. Our goal is to move beyond static data warehousing toward a dynamic, "agent-ready" data fabric that supports real-time clinical evaluation at enterprise scale, in a HIPAA-regulated environment. The platform is early and much of it is still being built, which is why we are looking for someone with high ownership and a strong self-starting instinct rather than someone waiting for a fully specified backlog. What You'll Do Build the pipelines that feed the agents. Develop the systems that fetch, parse, and serve both structured and unstructured data in formats optimized for real-time inference — spanning clinical EHR records, high-throughput genomics (NGS), and cardiovascular imaging (Echo, Cath, ECG). Own the warehouse and its transformations. Build and maintain our dbt models on BigQuery, along with the tests, documentation, and SQL standards that keep a growing model layer trustworthy. Model the multi-modal patient record. Shape the data model across those domains, applying normalized and dimensional design as each one demands, and write the code that enforces it. Move data through event-driven services. Build and operate the Pub/Sub topics, subscriptions, and dead-letter handling that connect ingestion, evaluation, and result delivery, with the retry and idempotency behavior that reliability at scale requires. Make the data agent-ready. Build the data access patterns and metadata layers that let AI agents autonomously discover, query, and reason over structured and unstructured datasets, and the retrieval services those agents call. Write the software on top. Build the TypeScript services and APIs that handle agent input and output and coordinate specialized agents, meeting the platform's performance and scalability demands. You are expected to be comfortable in the application codebase, not only in the data layer. Own the infrastructure your platform runs on. Extend and operate the platform's Google Cloud footprint in Terraform — BigQuery datasets, Pub/Sub, Cloud SQL, Cloud Storage, Memorystore, Secret Manager, and the service-account and IAM model that governs access to clinical data. Scale across hospital networks. Build for federated networks of hospitals: multi-tenancy, high availability, and performance across hybrid on-prem and cloud environments built for sensitive health-system integrations. Guarantee ground truth. Implement automated solutions to monitor data quality and lineage with strict traceability back to source systems, ensuring "ground truth" for agentic evaluations. Instrument for trust. Build the observability, error tracking, and human-in-the-loop checkpoints that make automated clinical evaluation transparent and debuggable. Raise the standard around you. Partner with clinical, analytics, and platform engineering teams on data modeling standards, governance, and practices for maintaining data integrity in a HIPAA-regulated environment. How You Work We care about these as much as the technical checklist. High ownership. You own what you build all the way into production — you care whether it stays up, you chase root causes instead of symptoms, and you do not treat the deploy boundary as the end of your responsibility. Self-starter. The problem space is genuinely open. You are comfortable identifying the most valuable next thing and starting on it without a fully specified ticket, and you surface ambiguity early rather than stalling on it. Collaborative. You work directly with clinical, analytics, and platform engineering partners. You write things down, you explain trade-offs to non-specialists, and you make the people around you faster. Quick to add impact and value. You bias toward shipping something real and incremental early over long design cycles, and you look for the change that moves the platform now. Our Stack You will not have used all of this, and we do not expect you to have. It is here so you know what you would be working in. Warehouse and transformation: BigQuery, dbt Operational data stores: Cloud SQL (PostgreSQL), Memorystore (Redis), Cloud Storage Messaging: Pub/Sub with dead-letter queues Healthcare data: Google Cloud Healthcare API FHIR stores, HL7/FHIR, DICOM, Avro Languages: Python for data pipelines and transforms; TypeScript on Node for platform services and APIs Application frameworks: NestJS, TypeORM Infrastructure: Terraform, Docker, Secret Manager, service-account and IAM-based access control Decisioning: GoRules ZEN engine for versioned decision models Cloud: primarily Google Cloud, with some AWS at the edges What We're Looking For Data engineering depth. Proven track record building and operating production data pipelines that handle structured and unstructured data at scale, with real ownership of reliability and correctness. Google Cloud fluency. Hands-on experience designing and running workloads on GCP — BigQuery, Pub/Sub, Cloud Storage, Cloud SQL, and Secret Manager — including the IAM and service-account model that controls access to sensitive data. Analytics engineering. Strong SQL and practical experience with dbt or an equivalent transformation framework, including testing, documentation, and managing a model layer as it grows. Infrastructure practice. Comfort owning infrastructure as code in Terraform, working in containers, and taking responsibility for the operational characteristics of what you deploy. Software engineering ability. You write production-quality application and service code, not just pipeline glue — including APIs, tests, and the design work that goes with them. Python and TypeScript. Python strong enough for production pipelines as well as hands-on data profiling and debugging, plus enough TypeScript or another statically typed language to work confidently in our service and application code. Event-driven systems. Experience with pub/sub or queue-based architectures and the failure modes that come with them — retries, ordering, idempotency, and dead-letter handling. Interoperability standards. Working knowledge of HL7, FHIR, and Epic/Cerner data structures, along with DICOM and genomic data formats. Regulatory fluency. Familiarity with building secure, resilient systems under HIPAA and SOC 2. Experience Requirements Total Professional Experience: 5+ years building data-intensive software systems in production. Data Engineering: 3+ years focused on data engineering, pipeline ownership, or data modeling, ideally in the healthcare or life sciences domain. Cloud Infrastructure: 2+ years hands-on building and operating on Google Cloud, with demonstrated ownership of infrastructure decisions rather than consuming someone else's. Healthcare Domain: 2+ years in HIPAA-regulated environments, with hands-on exposure to EMR integrations (Epic, Cerner) and healthcare data standards. AI/ML Orchestration: 1+ years hands-on building with Large Language Models — agentic workflows, RAG, or autonomous tool use. Data at Scale: Demonstrated experience managing structured (SQL, NoSQL) and unstructured data at a scale of millions of records, ensuring data integrity for downstream AI consumption. Education Primary Requirement: Bachelor's degree in Computer Science, Software Engineering, Data Science, Health Informatics, or a related technical field. Preferred: Master's degree or Ph.D. in Computer Science (AI/ML or distributed systems focus) or Biomedical Informatics. Alternative Background: Equivalent professional experience — including a portfolio of significant open-source contributions or industry-recognized technical writing — will be considered. Bonus Points Google Cloud Healthcare API. Direct experience with managed FHIR or DICOM stores. Specialized clinical data. Direct experience with OMOP, DICOM, genomic data models, or longitudinal patient records. Kubernetes. Experience running containerized workloads on Kubernetes. AI data engineering. Experience with vector databases (Pinecone, Weaviate, pgvector) or graph databases to support RAG and agentic memory. AWS. Experience with AWS services alongside GCP in a multi-cloud environment. Advanced modeling techniques. Experience with Data Vault 2.0, Master Data Management, or comparable enterprise modeling methodologies. CHI: $125,000-$180,000 The expected salary range may vary for other locations. Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Data Scientist II, Real World Evidence, Life Scien...

Negotiable

Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. The Real World Evidence (RWE) group within the Life Sciences R&D team at Tempus works with major pharmaceutical partners to provide best-in-class data, analysis, and methodological guidance for Tempus’s real-world data offering. We are seeking a highly motivated and solutions-oriented RWE Data Scientist II with experience and interest in oncology and epidemiological study design to join our team. This role requires the ability to lead observational studies, derive insights from complex real-world clinical data, implement advanced statistical methods, and leverage cutting-edge AI tools to scale tasks and augment insights. Key Responsibilities Pharma Collaboration & Strategy: Partner with pharmaceutical collaborators to independently execute robust RWE research plans that leverage the Tempus multimodal platform to address key questions in trial design and outcomes research. Real World Data Expertise: Lead the derivation of complex real-world endpoints using extensive coding, demonstrating deep comprehension of Tempus clinical and molecular data structures and complexity, while also serving as an expert on the methodological nuances and limitations of real-world data. Methodology & Platform Contribution: Stay up-to-date on methodological advancements in real-world studies (e.g., causal inference, survival analysis) and oncology guidelines (NCCN and ongoing clinical trials) to contribute to reusable code, internal packages, and best practices that can be applied across multiple collaborations. AI & LLM Innovation: Incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation. Scientific Interpretation & Communication: Interpret results of RWE analyses to draw appropriate inferences based on study design/statistical methods, while also evaluating study limitations. Communicate complex methods and results clearly to both technical and non-technical stakeholders. Prepare and present internal reports, external-facing deliverables, and, where appropriate, manuscripts or conference materials. Cross-Functional Collaboration: Collaborate with internal product, oncology, and clinical abstraction, and real-world data science teams to continually enhance Tempus data quality, products, and analytical best practices. Minimum Qualifications Education: Education in epidemiology, biostatistics, data science, public health, or a related field, to the level of either: PhD Master’s degree and 2+ years of additional work experience Technical and Statistical Proficiency Proficiency with observational real-world healthcare data, including analytical experience with time-to-event methodologies (survival analysis). Proven expertise in executing RWD analytical studies. Proficient in using R and SQL, especially statistical tools and packages. Proficiency applying machine learning, LLM-based coding assistants (e.g., Claude Code, Copilot, Cursor) and agentic frameworks to support data analysis, code review, or scientific documentation workflows. Adherence to good software engineering practices (version control, modular code, documentation). Communication & Client Focus: Demonstrated experience interfacing with clients, showcasing adeptness in presenting and tailoring messaging to a variety of stakeholders. Soft Skills: Excellent written and verbal communication skills with strong project management skills. Ability to thrive in a fast-paced, dynamic environment working with multi-disciplinary scientists on complex problems. Preferred Skillsets Experience working with Pharma or drug development. Experience in clinical trial design (particularly Phase II-III) in the clinical development space. Analytical proficiency with claims, EHR, or registry data. Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work. Knowledge of oncology guidelines (e.g., NCCN). Experience with biomarker or molecular data (e.g., genomics). Experience with cloud platforms such as AWS and/or BigQuery and/or Google Cloud Platform (GCP). #LI-DA1 CHI: $100,000-$140,000 NYC/SF: $110,000-$150,000 The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. Additionally, for remote roles open to individuals in unincorporated Los Angeles – including remote roles- Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Software Engineer, Coding Evaluation & Training Da...

Negotiable

ABOUT US Our mission is to raise AGI with the richness of human intelligence — curious, witty, imaginative, and full of unexpected brilliance. Surge was founded by engineers and researchers who dreamed of building the next generation AI. We're building a platform that powers the most powerful models in the world in partnership with companies like, Anthropic, Google, Microsoft, and Meta. At Surge, we believe the path to AGI isn't just about scaling compute—it's about embracing the unlimited ceiling of human intelligence and creativity in the data that shapes these systems. Our platform combines elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations that go beyond benchmarks. We've run a profitable business from day one without raising venture funding. THE ROLE As a Software Engineer, Coding Evaluation & Training Data at Surge, you'll sit at the intersection of software engineering and product to build and run the systems that teach frontier models how to code. You’ll own end-to-end coding data projects for top AI labs, designing tasks, RL environments, and evaluation schemes that reflect real-world software engineering. This is an ideal role for a software engineer who wants to keep using their engineering skill set every day, but is most excited by training data quality, agentic evaluation, and system design (humans + models + tools) rather than traditional product feature work. WHAT YOU'LL DO - Own end-to-end coding data projects, from initial scoping and pilot design through execution, iteration, and scale-up. - Design agentic training workflows and task structures that mirror real-world SWE work (e.g., refactoring, debugging, code review, large-repo navigation, tool use). - Define and iterate on rubrics, golden sets, and reward signals that capture true engineering value, not just “does it compile.” - Evaluate data and worker output with strong SWE taste, and make calls about what meets the bar for frontier training. - Design and run qualification processes for coding workers, including hands-on assessments of their coding ability. - Set up or partner on complex technical environments (e.g., containers, repos, test harnesses, sandboxes, code execution infrastructure). - Partner with technical staff at our clients to translate high-level training goals into concrete projects and technical environments. - Collaborate closely with Surge engineering, product, and operations to improve our coding data products, internal tools, and execution processes. WHAT WE'RE LOOKING FOR - 2–6+ years of professional software engineering experience building and maintaining real systems. - Strong coding ability in at least one mainstream language and comfort working in production codebases. - High “taste” for good engineering: you care about correctness, code quality, and how real engineering teams actually work. - Ability to reason about and debug technical environments, including containers, dependencies, and automated test setups. - Interest in owning projects end-to-end, including scoping, workflow design, execution, and continuous improvement. - Excellent written and verbal communication skills, with the ability to speak credibly with senior client engineers and translate fuzzy goals into actionable plans. - Excitement about AI/ML systems and the role of data, evaluation, and reward design in improving agentic coding capabilities.

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Senior SDET (Mobile-First Full Stack)

Negotiable

What we want to accomplish and why we need you Suki is a leading technology company that provides AI voice solutions for healthcare. Its mission is to reimagine the healthcare technology stack, making it invisible and assistive to lift the administrative burden from clinicians. Its flagship product is Suki Assistant, an AI assistant that uses generative AI to automatically create clinical documentation by ambiently listening to patient-clinician conversations. Suki helps clinicians complete notes 72% faster on average, assists with other tasks including coding and answering questions, and generates incremental revenue for organizations, delivering a 9X ROI in year 1. Suki also offers its proprietary AI and speech platform, Suki Platform, to partners who want to create best-in-class ambient and voice experiences for their solutions. Clinicians that use Suki already spend over 70% less time on administrative tasks, and we’re striving to do even better. Come and join us! We are a user-driven company and are committed to making sure every pixel of our product is in service of the doctors. We’re a team of technologists, clinicians, and industry experts working together to push the limits on technology used in medicine. We’re confident enough to move fast and talented enough not to break things. What will you do every day? Mobile (Primary Focus): - Own and evolve the end-to-end mobile test strategy for iOS and Android, ensuring coverage for mobile-specific concerns: offline and poor-network scenarios, background/foreground transitions, audio streaming and capture, permissions, gestures, and device fragmentation. - Architect and maintain mobile automation frameworks using Appium (or equivalent), with an emphasis on maintainability, execution speed, and stability on real-device and cloud-device grids. - Drive mobile performance, battery, memory, and stress testing across a representative device matrix, and set the quality bar for release readiness. - Investigate complex, cross-platform mobile issues — including native/hybrid layer, network, and audio pipeline problems — and drive them to root cause. Web and Backend (Secondary Focus): - Extend automation coverage to Suki's web surfaces so a single test strategy spans mobile, web, and service layers rather than three disconnected suites. - Design and implement API and service-level automation across REST, gRPC, and GraphQL, including bidirectional streaming and protocol buffers. - Build tests for event-driven and asynchronous flows (Kafka or similar) and for the microservices that the mobile client depends on. - Contribute to backend performance and load testing (K6, Locust, or similar) so that client-side quality claims hold under production-like load. Craft, Leadership, and AI: - Integrate mobile, web, and backend suites into CI/CD pipelines so that signal is fast, trustworthy, and actionable, with meaningful reporting and flake management. - Drive the design and code review process for test automation, and prioritise maintenance and tech debt in the frameworks you own. - Break complex test engineering work into components that early-career engineers can own, then mentor them through it. A significant share of our team is early in their careers, and your coaching is a core part of the job. - Keep strong documentation of how test design and architecture evolve over time. - Participate actively in SDET hiring, and in prioritising cross-team automation initiatives. - Lead initiatives that apply LLMs and agentic AI to the testing lifecycle — test generation, triage, self-healing locators, and coverage analysis — and help the team assess which tools actually move productivity. Requirement : - 7+ years as an SDET or equivalent, with a substantial portion focused on mobile test automation and QA process ownership. - Deep expertise in mobile automation: Appium or equivalent, real-device and cloud-device execution, and mobile debugging tools (Xcode Instruments, Android Studio profilers, Charles Proxy, Firebase). - Working knowledge of Swift, Kotlin, and Java in an automation context. - Demonstrated full-stack testing ability: you have written and owned web UI automation (Playwright, Selenium, or similar) and service-level automation against REST, gRPC, or GraphQL APIs. - Proficiency in at least one object-oriented language (Java preferred) and comfort with Python for scripting and data validation. - Solid understanding of microservices, cloud platforms (GCP preferred), containers, and CI/CD systems such as Buildkite or Jenkins. - A track record of mentoring engineers and leading automation projects to completion. - Strong analytical and communication skills, and the self-direction to operate in a fast-moving environment. - BA/BS in Computer Science, Information Science, AI/ML, or a related engineering field; MS/PhD a plus. Good to Have: - Exposure to AI/ML quality engineering. Any hands-on experience validating AI/ML models, pipelines, or data-driven systems is a strong plus — for example, evaluating ASR or NLP output, measuring Word Error Rate, intent classification accuracy, or hallucination rates, or reasoning about precision, recall, and model drift. This is a growth area for the role, not an entry requirement; we will invest in ramping the right mobile-strong candidate. - Experience building evaluation harnesses or "model-to-model" validation for production ML systems. - Experience with agentic AI tooling (MCP servers, RAG-based tools, autonomous test agents) applied to the SDLC. - Accessibility and localisation testing on mobile. - Prior start-up experience. *We don’t necessarily expect to find a candidate that has done everything listed, but you should be able to make a credible case that you’ve done most of it and are ready for the challenge of adding new skills to your resume. Tell me more about Suki - On a roll: Named by Fast Company as one of the most innovative companies, named Google’s Partner of the Year for AI/ML, named by Forbes as one of the top 50 companies in AI . - Great team: Founded, managed, and backed by successful tech veterans from Google and Apple and medical leaders from UCSF and Stanford. We have technologists and doctors working side-by-side to solve complex problems. - Great investors: We’re backed by Venrock, First Round Capital, Flare Capital, March Capital, Hedosophia and others. With our $165M raised so far, we have the resources to scale. - Huge market: Disrupting a massive, growing $30+ billion market for transcription, dictation, and order-entry solutions. Our vision is to become the voice user interface for healthcare, relieving the administrative burden on doctors instead of adding to it. - Great customers: Our solutions are used in health systems and clinics across the country, supporting clinicians across dozens of specialties. Check out what one of our users says about how Suki has helped his practice. - Impact: You’ll make an impact from day one. You’ll join a team working towards a shared purpose with a culture built upon deep empathy for doctors and passion for making their lives better. Suki is an Equal Opportunity Employer. We are dedicated to building a company that fosters inclusion and belonging and reflects the diverse communities we serve across the country. We know we are stronger this way, and we look forward to growing our team with these shared values.

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Staff Product Manager - Infrastructure, Data & Sec...

Negotiable

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! The Role: We are looking for a Technical Product Manager to own the roadmap and execution across Snorkel AI's Infrastructure organization, spanning three engineering pods: Core Services, Developer Experience, and Security. This is not a customer-facing product role, you will be the PM for internal platforms, data infrastructure, and security systems that the rest of engineering and the business depend on. You will be the single point of coordination across a broad surface area that currently lacks dedicated product leadership. What You Will Do: The Infrastructure org owns the data platform, event systems, observability, developer tooling, release pipelines, and the full security stack. The engineering leadership and pod leads are very technical but the org needs a PM who can translate business and compliance requirements into prioritized infrastructure investments, manage dependencies across pods and partner teams, and hold the line on scope and delivery timelines. Today, prioritization decisions are made ad hoc by engineering leads who are also deep in technical execution. This role gives the org a dedicated owner for roadmap clarity, stakeholder alignment, and cross-pod coordination. Business context and infrastructure strategy: - Develop a deep understanding of Snorkel AI's business, how the datasets are sold, and how should the platform be deployed, and scaled to support customers across AI labs, enterprise and federal and use that context to work closely with engineering leaders and product PMs to translate business scaling, security and functional needs into deployment patterns, and go-to-market requirements into concrete, prioritized projects for the Infrastructure org. Without this, infrastructure roadmaps become technically interesting but disconnected from what actually moves the business forward. Roadmap and prioritization across all three pods: - Work with engineering leads to define quarterly roadmaps for Core Services (data platform, metrics platform, event systems, observability, fraud detection, infrastructure cost management, platform customization infrastructure, SDS project infrastructure), Developer Experience (CI/CD, release pipelines, dev systems, AI dev tooling), and Security (Auth0 migration, AuthN/AuthZ, cloud security, encryption). - Make trade-off decisions when pods compete for shared resources or when new asks land mid-quarter. The org carries significant surface area relative to headcount. Your job is to ensure the team is working on the highest-leverage problems, not just the loudest requests. Stakeholder management and requirements gathering: - Be the interface between the Infrastructure org and its consumers: product engineering teams who depend on Dataset APIs, Platform APIs, and Packaging APIs; the data science and analytics teams who depend on Cascade and the transformation layer; GTM and customer success teams who surface compliance and security requirements from enterprise and federal customers. - Translate customer compliance requirements (SOC 2, FedRAMP, GDPR) into concrete security and data governance work items with clear acceptance criteria. Adoption and internal evangelism: - Proactively engage product PMs and engineering leads to ensure teams are leveraging infrastructure services, SDKs, and shared libraries (unified data access, event bus, security SDKs, CI/CD tooling) rather than building one-off solutions. - Track adoption metrics, SDK integration coverage, service onboarding rates, migration completions, and identify teams that are lagging or working around infrastructure offerings. Close the loop by either driving adoption or feeding unmet requirements back into the roadmap. - The Infrastructure org's impact is only as real as its adoption. You own the push to make sure the work doesn't just ship, it lands. Cross-pod coordination on foundational initiatives: - Drive initiatives that span multiple pods, such as the unified data access library (which touches Core Services for the library itself, Security for auth/RBAC/encryption enforcement, and Developer Experience for SDK distribution and testing tooling). - Coordinate the event bus rollout across services, ensuring schema governance, consumer onboarding, and observability are sequenced correctly. - Own the Auth0 migration program as a cross-functional deliverable that touches Security, Core Services, and every API team. Metrics, reporting, and visibility: - Define and track key metrics across pods: data platform query latency, event bus propagation latency, CI pipeline duration, release rollback rate, security finding remediation time, encryption coverage, infrastructure cost per workload. - Provide regular reporting to VP-level leadership on infrastructure org health, delivery status, and risk areas. - Maintain a clear view of staffing gaps and make the case for headcount investments backed by roadmap impact. Infrastructure cost and efficiency: - Partner with Core Services on infrastructure cost visibility, attribution, and optimization. Own the narrative around infrastructure spend not just tracking it, but driving decisions on reserved capacity, right-sizing, and cost allocation by team and product. - Own inference cost management as a cross-functional concern that touches both infrastructure and product economics. Minimum Qualifications: - 7+ years of product management experience, with at least 2 years in infrastructure, platform, or developer tools PM roles. You have shipped internal platforms or infrastructure products, not just customer-facing features. - Strong technical fluency. You don't need to write Terraform modules, but you need to understand what an event bus is, why CDC lag matters, what RBAC enforcement means at the data layer, and why audit logs shouldn't live in the production database. You can hold your own in architecture discussions and push back on technical proposals with substance, not just process. - Experience with compliance and security programs. You have worked on SOC 2, FedRAMP, or similar compliance frameworks and understand how to translate audit requirements into engineering work items. You know the difference between checking a box and actually being secure. - Comfort with ambiguity and breadth. This role covers platform infrastructure, data infrastructure, developer tooling, and security, three domains that each could be a full-time PM role at a larger company. You are comfortable context-switching, prioritizing ruthlessly, and saying no to work that doesn't clear the bar. - Experience working with small, high-leverage engineering teams.You understand that with small teams, every quarter's roadmap is a zero-sum game, and your job is to make sure the bets are right. - Fluency with AI-assisted workflows is a plus. The org treats AI tooling (Claude Code, Cursor) as a baseline expectation for engineers; a PM who understands and can evaluate these tools' impact on developer productivity will be more effective in this role. Preferred Qualifications: - 7+ years of experience as a Product Manager, including 3+ years focused on ML/AI products, MLOps, Platform infrastructure or data systems products. - Experience with agent-based systems, generative AI, reinforcement learning, self-improving systems, or automated workforce/task routing platforms. - Familiarity with LLMs, LLM Evaluations, human-in-the-loop systems, and synthetic data generation. - Background in Computer Science, Security or related technical field. - Demonstrated ability to lead large, cross-team initiatives without formal authority. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $260,000 - $300,000 USD Be Your Best at Snorkel Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success. Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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Robotics Engineer, Manipulation

Negotiable

Company Overview At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects. Position Overview We are seeking a versatile Robotics Engineer to develop and implement software solutions for our manipulation systems. The ideal candidate will have experience in controls, perception, and planning. You will be working on deploying state-of-the-art learning-based algorithms on real robot setups, focused on daily manipulation tasks. This role will require close collaboration with researchers and the ML team. The ideal candidate should be comfortable with building high-quality software for robotic manipulation and deploying it on robot hardware. Responsibilities - Help design algorithms, models, and techniques for various robotic manipulation tasks. - Design hardware and software systems for various robotic manipulation tasks. - Write controllers and perception stacks for real-world robotic deployment. - Write and maintain production-level C++ and Python code for our robotic manipulation platforms. - Collaborate with machine learning engineers to deploy state-of-the-art models on our robots. - Continuously improve and optimize robotic software for performance, reliability, and scalability. Preferred Qualifications - BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience. - Experience in designing robotic manipulation systems and deploying software on real robots. - Experience with writing controllers and knowledge of robotics (e.g., kinematics, dynamics, control, motion planning, SLAM). - Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc. - Systems-level understanding of the various software modules and their interfaces in a robotic application (ROS/ROS2, simulators, etc.). - Publications at top-tier ML, robotics or CV conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ECCV, ICCV). - Experience with designing custom robot hardware solutions. - Proficiency with various robot learning techniques (RL, imitation learning, etc.). - Experience working with modern computer vision algorithms, sensors (LiDAR, camera, radar, etc.), 3D (meshes, point clouds, etc.), segmentation, tracking, object detection. Base Salary Range $100,000 - $300,000 USD

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Software Engineer, Agent Runtime

Negotiable

ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. WHAT YOU'LL DO - Build the core systems that power agents. You’ll work on the Agent SDK — the orchestration engine, runtime, and primitives that define how agents reason, take actions, and interact with users and systems. - Design the agentic loop. What primitives do we need to build agents that are steerable and verifiable, while still conversational and adaptive? You’ll help define how agents operate in real-world environments. - Improve retrieval and grounding. You’ll build systems that ensure agents provide accurate, trustworthy responses by effectively retrieving and using knowledge. - Build evaluation systems. You’ll design frameworks that allow us and our customers to measure and improve agent quality over time. WHAT YOU'LL BRING - A passion for being on the frontier of AI products. - Strong software engineering fundamentals and experience building production systems. - Motivation and high-agency to drive outcomes in a high-autonomy culture. - 4+ years hands-on experience building production products and systems. - Degree in Computer Science or related field, or equivalent professional experience. EVEN BETTER - Experience building AI-powered systems or agent frameworks. - Experience with retrieval, evaluation systems, or LLM-based applications. - Experience building developer platforms or SDKs. - Experience with Go and Typescript. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

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Product Manager, Agent Development - Public Sector

Negotiable

ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. ABOUT THE PUBLIC SECTOR TEAM - Sierra recently received FedRAMP High https://sierra.ai/blog/certified-fedramp-high certification, enabling us to bring our industry-leading technology to partners in the US public sector. We’re now building our founding public sector team to redefine the relationship between the government and its citizenry. - Today, our agents already enable citizens to access social services, report fraud, and understand complex government systems. Simultaneously, they empower government officials to better hear the concerns and inquiries of their constituencies. - We are explicitly nonpartisan and committed to empowering public servants to leverage AI to advance their important missions. - In our journey, we are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and Family. These values are the foundation of our work, and we are committed to upholding them in everything we do. WHAT YOU'LL DO - Build enterprise-grade AI agents for government institutions: As an Agent Product Manager at Sierra, you will partner directly with our engineers and public sector customers to build and ship AI agents that serve people across high-stakes, mission-critical interactions. These agents will help constituents navigate complex programs, complete important tasks, and get timely, trustworthy support while meeting the security, accessibility, and reliability standards government work demands. - Discover where agents can make the greatest impact: Ground-up discovery is a critical part of the role. Work closely with federal and state institutions to understand their missions, operating constraints, constituent needs, and service-delivery workflows. Identify and prioritize new opportunities for AI agents to improve access, responsiveness, and outcomes. - Engage with customers at every level: Become a trusted advisor to public sector leaders and a hands-on partner to their working teams. Discover and assess requirements, prepare and present demonstrations, navigate technical and operational challenges with customer counterparts, and continuously improve agents to fit each institution’s policies and workflows. - Own outcomes from concept through production: Translate mission goals into a clear product strategy, roadmap, and measurable definition of success. Coordinate across customer stakeholders, engineering, design, research, security, and operations to take agents from initial concept through launch and ongoing iteration. - Shape the platform and public sector roadmap: Your direct work with government institutions will guide the evolution of Sierra’s core platform. Surface unmet needs, prototype new tools and features, and collaborate with research, product, and platform teams to shape the future of AI agent development for the public sector. - Help build Sierra’s public sector business: Partner closely with public sector leadership on market strategy, product investments, and repeatable approaches that allow the team to scale quickly while preserving the trust and craftsmanship our customers expect. EXAMPLE PROJECTS These are examples of projects our healthcare team has worked on: - Build agents that help people understand eligibility, apply for benefits, check case status, and navigate complex social-service programs. - Develop secure reporting experiences that make it easier to report fraud, waste, abuse, or other concerns and route each submission appropriately. - Create voice and chat agents that answer questions about licenses, permits, taxes, public records, and other high-volume government services across languages and channels. - Design proactive and long-horizon agents that keep constituents informed, collect missing information, and guide them through multi-step government processes over time. - Turn patterns in constituent conversations into actionable insights that help public officials understand emerging needs and improve service delivery. WHAT YOU'LL BRING - 5-7+ years of experience developing highly technical products. - Experience in Product Management or a similar product development role at or above the level of Senior Product Manager. - A passion for public sector work and a belief in the potential of our institutions to transform outcomes for the people they serve. - Comfort working directly with customers to understand their needs, navigate complex organizations, and solve real-world problems. A core part of the role is coordinating directly with public servants and technical stakeholders to understand their workflows. - Experience crafting and tailoring messages for different audiences, including senior executives and public sector leaders. - Ability to communicate highly technical concepts to both non-technical and technically proficient audiences, including recent developments in AI. - Strong product judgment and a track record of driving ambiguous, cross-functional initiatives from discovery through launch and measurable impact. - A degree in a technical or related field, or equivalent professional experience. EVEN BETTER... - Experience building or deploying AI/LLM products, especially conversational or long-horizon agents, and familiarity with evaluation, retrieval, prompt engineering, or agent tooling. - Experience with government technology or working with public sector institutions, including an understanding of procurement, regulation, security, accessibility, and patterns of partnership. - A demonstrated track record of operating in ambiguity, for example as part of a founding team, a new initiative within a large organization, or a forward-deployed product or engineering team. - Some coding experience with React, TypeScript, and Go, or experience prototyping technical products directly with engineers. - An MBA or equivalent professional experience at the intersection of technology, operations, and strategy. - Past roles that involved working directly with end customers to influence software development. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

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Staff Engineer, AI Operations & Governance, Workpl...

Negotiable

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. This is a deeply technical individual contributor role reporting to the Head of AI Operations & Governance (Enterprise AI). This person will own significant portions of the technical operating load for workplace AI: platform configuration, connector and integration management, observability wiring, secrets and access hygiene, model/prompt lifecycle mechanics, and hands-on changes in production. At the same time, the role will help translate production realities into governance artifacts, risk assessments, status updates, and training/enablement support — giving the Head a force multiplier who can operate at both the technical and “softer” layers of AI operations and governance. This role will also serve as a hands-on technical responder for workplace AI incidents and production issues. The Staff Engineer is expected to investigate failures, troubleshoot across platforms, integrations, prompts, configurations, and access paths, implement mitigations or fixes where appropriate, and help restore service quickly while documenting root cause, lessons learned, and prevention steps. Key responsibilities: · Implement and maintain AI platform configurations Own day-to-day configuration of AI platforms and orchestration tools (models, routes, guardrails, tenants, policies, role mappings, prompt libraries, etc.). · Manage connectors, integrations, and data access paths Design, configure, and maintain connectors and extensions into SaaS systems, data sources, and workflow tools; ensure connectivity is reliable, secure, and aligned with access policies. · Own observability wiring for AI tools Set up and maintain logging, metrics, and alerts for AI workflows and tools; make sure key signals (latency, errors, usage, drift indicators) are captured and visible to the team. · Handle secrets and access hygiene Implement secure storage and rotation for API keys, tokens, and credentials; maintain access control configurations and partner with Security/IT on reviews and remediation. · Execute model and configuration changes in production Implement model swaps, policy updates, prompt changes, version upgrades, and rollout plans; maintain detailed change records and rollback paths. · Support technical evaluations and benchmarking Run experiments and benchmarks on models, tools, and configurations; collect and summarize technical performance data to inform governance and roadmap decisions. · Translate technical signals into governance and risk views Interpret logs, metrics, and incidents into clear risk, reliability, and compliance narratives that can be shared with Security, Legal, HR, and business sponsors. · Contribute to playbooks and training Co-author technical sections of operational playbooks, runbooks, and training materials; occasionally participate in training or office hours to help users understand capabilities and guardrails. · Coordinate and communicate on incidents and changes Act as a technical point of contact in incidents: triage, investigate, propose mitigations, execute fixes, and document learnings; communicate clearly with non-technical stakeholders when needed. Required qualifications: · 8+ years in roles such as platform engineer, DevOps/SRE, ML/AI operations, or technical SaaS operations, with hands-on responsibility for production systems. · Strong fluency in APIs, integrations, and infrastructure-as-config concepts; able to work in code/JSON/YAML configuration environments and with automation where appropriate. · Hands-on experience with monitoring and observability tools (logs, metrics, alerts) and using them to diagnose issues and guide improvements. · Practical experience working with at least one class of AI or automation platforms (LLM providers, AI productivity tools, RPA/workflow engines, or similar). · Comfort with secure secrets management and access control practices (roles, permissions, key rotation, least-privilege patterns). · Ability to document technical work and decisions clearly for both technical and non-technical audiences. · Strong ownership mindset, bias to action, and comfort operating close to production in a high-stakes environment. Preferred qualifications: · Experience in ML/AI ops specifically (model deployment, evaluation, drift monitoring). · Familiarity with prompt engineering, policies/guardrails, and configuration patterns for LLM-based systems. · Exposure to governance, compliance, or risk frameworks for data-driven or AI systems. · Experience collaborating with Security, Legal, and business stakeholders on technical risk and mitigation. · Prior involvement in incident response, change management, or on-call rotations for critical systems.

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Head of Product Design (R5776)

Negotiable

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. What you'll do: User Research & Product Discovery Lead customer research efforts across product development, customer engagement, and solutions teams to identify pain points, workflows, unmet needs, and product opportunities. Establish scalable mechanisms for gathering, synthesizing, and communicating user insights throughout the product development lifecycle. Translate customer and operational feedback into actionable product recommendations, requirements, and design initiatives. Product Design & User Experience Design end-to-end workflows across autonomy development, simulation, testing, analysis, deployment, and operations. Create intuitive experiences for highly technical users including autonomy engineers, software developers, operators, and mission planners. Simplify complex system architectures, data flows, infrastructure tooling, and AI workflows into understandable user experiences. Own the end-to-end experience of the product including training, documentation, tutorials, and overall workflows – help our users get the most of the product. Strategic Product Leadership Partner closely with Product Managers to shape product vision, roadmap priorities, and go-to-market strategies. Drive alignment across Product, Engineering, Design, and Executive Leadership through compelling narratives, prototypes, and visual storytelling. Advocate for user-centered decision making in a highly technical and engineering-driven environment. Design Practice, Systems & Organizational Scale Establish and evolve design standards, design systems, UX principles, and product design best practices across Shield AI. Raise the quality bar for product design, usability, consistency, and customer experience. Grow the product design function by mentoring a small team of designers, shaping design culture, and staying actively involved in critical design work. Champion design wins and impact across the organization, highlighting the important role design plays in delivering successful products. AI-Native Product Design & Agentic Workflows Define the future user experience for AI-assisted autonomy development, simulation, testing, deployment, and operational workflows. Design human-AI collaboration models that enable users to effectively leverage AI agents, copilots, and foundation-model-powered systems. Partner with Product, AI Research, and Engineering teams to create new interaction patterns for agent orchestration, autonomy generation, scenario creation, test generation, and mission planning. Develop frameworks for trust, transparency, explainability, and human oversight within AI-assisted workflows. Help establish industry-leading design patterns for Physical AI development platforms and agent-driven software systems. Required qualifications: 10+ years of experience in Product Design, UX Design, Human Factors, or related fields. Proven track record designing and shipping complex technical products, developer tools, platforms, infrastructure software, data-dense applications, or enterprise systems. Deep technical fluency and ability to effectively collaborate with senior engineering leaders on architecture, technical constraints, and software development workflows. Strong experience conducting user research and translating insights into product strategy and design solutions. Proven ability to influence product direction and organizational decisions without direct authority. Strong understanding of AI-powered product experiences, including conversational interfaces, human-in-the-loop workflows, AI-assisted decision making, and agent-based systems. Experience defining new user experiences in ambiguous technology spaces where industry standards and workflows are still emerging. Experience building and retaining high-performing design teams. Strong portfolio showcasing both strategic thinking and hands-on design execution. Preferred qualifications: Background designing developer platforms, SDKs, cloud infrastructure products, DevOps tooling, or engineering productivity tools. Direct work on AI/ML platforms, MLOps workflows, foundation-model-enabled products, copilots, or agentic systems. Familiarity with autonomy, robotics, simulation, digital twins, mission systems, physical AI, or related development workflows including testing, evaluation, deployment, and operations. Portfolio examples involving command-and-control systems, mission-planning interfaces, operational dashboards, or real-time decision-support tools. Ability to design across integrated hardware/software systems. Domain exposure to defense technology, aerospace, robotics, or other regulated, mission-critical, or high-consequence operational environments.

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Perception and Autonomy Engineer

Negotiable

Saronic Technologies is a leader in revolutionizing autonomy at sea, dedicated to developing state-of-the-art solutions that enhance maritime operations through autonomous and intelligent platforms. JOB OVERVIEW We are seeking a Perception Engineer to play a pivotal role in designing, developing, and implementing perception systems for our autonomous surface vessels. Our team is laser focused on making boats go and perform tasks with no human involvement. This job is available at multiple levels, including entry, senior, and staff. RESPONSIBILITIES: - Develop algorithms and models which allow boats to sense and navigate - Develop metrics which allow quantitative analysis of improvements and regressions in boat performance - Analyze and work with large data systems to enable model training and evaluation REQUIREMENTS: - Strong programming fundamentals - Extensive programming experience and demonstrated ability to work on large systems - Computing Fundamentals - A general understanding of operating systems and or similar large scale systems - An understanding of basic computer architecture - A demonstrated willingness to learn and pivot based on new information USEFUL SKILLS: - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) - Understanding of various filters and their applications - Proficiency in Rust - Experience with maritime or autonomous vehicle projects - Experience with signals processing or sensor fusion - Experience with low latency inference and tracking pipelines - Experience with path planning algorithms - Experience training and deploying multi modal models - Experience with various sensors including radar, cameras, and lidar - Experience developing and optimizing deployed ML systems PHYSICAL DEMANDS: - Prolonged periods of sitting at a desk and working on a computer - Occasional standing and walking within the office - Manual dexterity to operate a computer keyboard, mouse, and other office equipment - Visual acuity to read screens, documents, and reports - Occasional reaching, bending, or stooping to access file drawers, cabinets, or office supplies - Lifting and carrying items up to 20 pounds occasionally (e.g., office supplies, packages) BENEFITS: - Medical Insurance: Comprehensive health insurance plans covering a range of services - Dental and Vision Insurance: Coverage for routine dental check-ups, orthodontics, and vision care - Saronic pays 100% of the premium for employees and 80% for dependents - Time Off: Generous PTO and Holidays - Parental Leave: Paid maternity and paternity leave to support new parents - Competitive Salary: Industry-standard salaries with opportunities for performance-based bonuses - Retirement Plan: 401(k) plan - Stock Options: Equity options to give employees a stake in the company’s success - Life and Disability Insurance: Basic life insurance and short- and long-term disability coverage - Additional Perks: Free lunch benefit and unlimited free drinks and snacks in the office If this role is based in the United States, it requires access to export-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3) https://www.govinfo.gov/link/uscode/8/1324b. Saronic does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits. We are also committed to providing reasonable accommodations for qualified individuals with disabilities.

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Senior ML Systems Engineer, Inference

Negotiable

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on. We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale. Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers. We're looking for a ML Systems Engineer, Inference. We want Runpod to be the best place in the world to run LLM inference, meaning the fastest and the most cost-efficient. You'll lead that effort. You'll own LLM serving performance end to end. That means measuring it, understanding it, and improving it across models, hardware generations, and workloads. The work you ship will show up directly in the latency and cost our customers experience. This is a hands-on engineering role for someone who likes finding the real bottleneck and fixing it, then turning that fix into something that runs reliably in production. RESPONSIBILITIES - Define how we measure inference performance, including throughput, time to first token, inter-token latency, and cost per token, and build the tooling that makes those measurements rigorous and repeatable. - Profile and diagnose performance problems across the serving stack, from scheduling and memory management down to kernels and interconnect. - Improve serving efficiency for large, state-of-the-art models on single-node and multi-node GPU deployments. - Turn what you learn into production-ready runtimes, configurations, and defaults that customers benefit from automatically. - Work closely with product and infrastructure teams to shape how inference is offered on Runpod. - Keep up with the fast-moving inference ecosystem, including the open-source community, and decide what's worth adopting, what's worth building, and what's worth contributing back. - Trace bottlenecks in the serving engine/runtime and implement fixes when configuration tuning is not enough. REQUIREMENTS - 5+ years of professional system engineering experience. - Deep, hands-on experience with vLLM, SGLang (or a comparable serving engine) in production or at serious benchmark scale.a - Strong software engineering skills in Python. You're comfortable working in large, performance-critical codebases. - A solid understanding of what drives LLM inference performance: batching, memory, parallelism, and the trade-offs between latency and throughput. - Experience with modern inference optimization techniques such as quantization, speculative decoding, or distributed serving. - Rigor in benchmarking and performance analysis, plus comfort with GPU profiling tools. - The ability to explain your results clearly in writing and turn them into decisions. PREFERRED - Experience writing or tuning GPU kernels in CUDA or Triton. - Contributions to inference or ML systems projects. - Experience with multi-node GPU systems and high-speed networking. - Experience at a company where inference cost and latency were core business metrics. What You’ll Receive: - The competitive base pay for this position ranges from ($150,000 - $220,000). This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location - Meaningful equity in a fast-growing company- everyone on the team receives stock options — your impact drives our growth, and you share in the upside. - Generous medical, dental & vision plans - Flexible PTO- take the time you need to recharge - Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication - Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale. - $1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work in the United States; however, we are currently unable to sponsor employment visas.

R

Forward Deployed Engineer EMEA

Negotiable

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on. Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers. We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale. As part of the Revenue Team, you’ll work closely with customers, the sales team, the product team, the engineering team and the support team to ensure a seamless and delightful Runpod experience. Your role will focus on providing technical insights, resolving challenges, and building trust with customers. Our team is dedicated to delivering exceptional solutions and fostering collaboration across functions to make the platform as smooth and efficient as possible. This Forward Deployed Engineer will support the growth of our cloud platform. This role ensures customers experience seamless operations, addressing technical challenges, guiding onboarding, and collaborating with engineering and sales teams. Ideal candidates combine technical expertise with strong communication skills to enhance customer satisfaction and improve our platform. You’ll play a key role in solving technical challenges, shaping the customer experience, and driving product improvements. By building strong customer relationships and collaborating across teams, your contributions will directly enhance operational efficiency, customer satisfaction, and the success of Runpod. This FDE will need to be located in EMEA Responsibilities: - Participate in sales meetings with customers, explain Runpod’s specific technologies, provide architectural recommendations, and build proof-of-concept solutions to support onboarding for potential high-spending customers. - Troubleshoot and resolve critical or complex technical issues escalated by customers, including those related to configuration, performance, functionality, compatibility, or code errors in Runpod’s products or services. - Utilize various tools and methods, such as code analysis, scripting for testing, log analysis and remote access, to identify root causes and deliver solutions or workarounds. - Communicate effectively with customers and internal teams, including engineering, sales, supply and product management teams, to ensure customer satisfaction and integrate valuable feedback. - Assist the support team in troubleshooting and resolving escalated technical tickets, collaborate with the engineering team to provide workarounds or bug fixes, and work with the infrastructure team to address GPU server-related issues. - Contribute to product development and testing efforts by relaying feedback from customers and the support team to the engineering team, helping to shape product improvements. - Create and maintain technical documentation, such as knowledge base articles, FAQs, guides, and manuals, while also developing and delivering training sessions, webinars, and demos for customers, partners, and internal teams. Requirements: - Bachelor's degree in a relevant field (e.g., Computer Science, Computer Engineering, Software Engineering, Information Technology, or a related field) or equivalent professional experience. - 3+ years of professional experience in software development. - Strong problem-solving skills and ability to work in a collaborative environment. - Familiarity with applied AI use cases such as inference, fine-tuning, LLM-based applications, or agentic systems. - Excellent communication skills and attention to detail. - Located in US in the PST region - Successful completion of a background check. Preferred: - Strong technical skills, including proficiency in Python (Django, Flask, PyTorch), JavaScript (React, Node.js), and Go, with hands-on experience in full-stack development. - Knowledge of various operating systems (Linux/Ubuntu), containerization (Docker), SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB), and an understanding of data center components such as server hardware and network operations, as well as familiarity with common network protocols like TCP/IP, HTTP/HTTPS, SSH, and DNS. - Proficiency in deploying and managing AI/ML models as services or APIs, along with a deep understanding of machine learning models, including their training, inference, and deployment processes on Runpod platforms. - Ability to write and execute scripts, queries, or commands, and the ability to read and understand code, logs, errors, or traces. What You’ll Receive: - The competitive base pay for this position ranges from 100,000- 180,000 usd (local currency equivalent). This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location - Meaningful equity in a fast-growing company- everyone on the team receives stock options — your impact drives our growth, and you share in the upside. - Flexible PTO- take the time you need to recharge - Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication - Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale. - $1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work; however, we are currently unable to sponsor employment visas

R

Forward Deployed Engineer APAC

Negotiable

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on. Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers. We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale. As part of the Revenue Team, you’ll work closely with customers, the sales team, the product team, the engineering team and the support team to ensure a seamless and delightful Runpod experience. Your role will focus on providing technical insights, resolving challenges, and building trust with customers. Our team is dedicated to delivering exceptional solutions and fostering collaboration across functions to make the platform as smooth and efficient as possible. This Forward Deployed Engineer will support the growth of our cloud platform. This role ensures customers experience seamless operations, addressing technical challenges, guiding onboarding, and collaborating with engineering and sales teams. Ideal candidates combine technical expertise with strong communication skills to enhance customer satisfaction and improve our platform. You’ll play a key role in solving technical challenges, shaping the customer experience, and driving product improvements. By building strong customer relationships and collaborating across teams, your contributions will directly enhance operational efficiency, customer satisfaction, and the success of Runpod. Responsibilities: - Participate in sales meetings with customers, explain Runpod’s specific technologies, provide architectural recommendations, and build proof-of-concept solutions to support onboarding for potential high-spending customers. - Troubleshoot and resolve critical or complex technical issues escalated by customers, including those related to configuration, performance, functionality, compatibility, or code errors in Runpod’s products or services. - Utilize various tools and methods, such as code analysis, scripting for testing, log analysis and remote access, to identify root causes and deliver solutions or workarounds. - Communicate effectively with customers and internal teams, including engineering, sales, supply and product management teams, to ensure customer satisfaction and integrate valuable feedback. - Assist the support team in troubleshooting and resolving escalated technical tickets, collaborate with the engineering team to provide workarounds or bug fixes, and work with the infrastructure team to address GPU server-related issues. - Contribute to product development and testing efforts by relaying feedback from customers and the support team to the engineering team, helping to shape product improvements. - Create and maintain technical documentation, such as knowledge base articles, FAQs, guides, and manuals, while also developing and delivering training sessions, webinars, and demos for customers, partners, and internal teams. Requirements: - Bachelor's degree in a relevant field (e.g., Computer Science, Computer Engineering, Software Engineering, Information Technology, or a related field) or equivalent professional experience. - 3+ years of professional experience in software development. - Strong problem-solving skills and ability to work in a collaborative environment. - Familiarity with applied AI use cases such as inference, fine-tuning, LLM-based applications, or agentic systems. - Excellent communication skills and attention to detail. - Located in the APAC region (Ideally in Malaysia, Singapore or South Korea) - Successful completion of a background check. Preferred: - Strong technical skills, including proficiency in Python (Django, Flask, PyTorch), JavaScript (React, Node.js), and Go, with hands-on experience in full-stack development. - Knowledge of various operating systems (Linux/Ubuntu), containerization (Docker), SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB), and an understanding of data center components such as server hardware and network operations, as well as familiarity with common network protocols like TCP/IP, HTTP/HTTPS, SSH, and DNS. - Proficiency in deploying and managing AI/ML models as services or APIs, along with a deep understanding of machine learning models, including their training, inference, and deployment processes on Runpod platforms. - Ability to write and execute scripts, queries, or commands, and the ability to read and understand code, logs, errors, or traces. What You’ll Receive: - The competitive base pay for this position ranges from 100,000- 160,000 usd (converted to local currency) This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location - Meaningful equity in a fast-growing company- everyone on the team receives stock options — your impact drives our growth, and you share in the upside. - Flexible PTO- take the time you need to recharge - Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication - Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale. - $1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work in the United States; however, we are currently unable to sponsor employment visas

R

Staff Software Engineer, Agentic Ads

Negotiable

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. ABOUT THE ROLE: Help turn “I built a business” into “I have customers.” As a Staff Software Engineer on Replit’s Money team, you’ll help shape Agentic Ads: an emerging area focused on using AI to make customer acquisition more accessible to businesses built on Replit. This role focuses on intelligent, advertiser-facing software—not ad-serving infrastructure, an ad exchange, or a real-time bidding engine. You’ll tackle problems at the intersection of AI, business growth, measurement, and trustworthy automation, turning complex decisions into clear, useful experiences. You’ll help define a new product area, working closely with product, design, and engineering to discover customer needs and deliver practical solutions. This is a hands-on Staff role: you’ll own architecture, lead delivery across teams, mentor engineers, and turn an ambitious vision into incremental customer value. YOU WILL: - Set technical direction for Agentic Ads, translating customer needs into an architecture and roadmap that balance rapid learning with long-term reliability. - Build agent-powered experiences that help businesses make informed growth decisions, translating user goals into understandable recommendations and appropriately authorized actions. - Design extensible integrations with external services, using well-defined APIs and abstractions that accommodate changing provider capabilities and requirements. - Partner with product and data teams to establish reliable measurement, connect product activity to business outcomes, and address data quality, delayed feedback, and uncertainty. - Develop intelligent automation that learns from performance signals and helps users improve outcomes within their goals and constraints. - Make financial control a product requirement: scoped authorization, explicit approvals, budget limits, auditable decisions, clear reporting, and reliable pause controls. Distinguish recommendations from actions that commit customer spend. - Make cross-platform workflows reliable with durable state, idempotency, safe retries, reconciliation, observability, and recovery from partial failures, expired credentials, rate limits, and provider API changes. - Establish agent evaluations and production monitoring for plan quality, tool selection, policy compliance, spend safety, and verified execution. Partner on experiments that distinguish attributed results from incremental customer value. - Lead ambiguous initiatives across product, design, AI, data, security, and partnerships. Stay hands-on with implementation, define incremental milestones, mentor engineers, and own the quality and operation of what the team ships. REQUIRED SKILLS AND EXPERIENCE: - A track record of Staff-level technical leadership on complex customer-facing products or platforms: setting direction, resolving ambiguity, and delivering through launch and production operation across multiple teams. - Strong hands-on software engineering and system design skills across APIs, data modeling, distributed systems, asynchronous workflows, and customer-facing product experiences. - Experience building and operating third-party integrations that remain correct under duplicate requests, partial failures, rate limits, changing schemas, and inconsistent external state. - Practical experience shipping LLM-powered products or agentic workflows, including tool use, orchestration, evaluations, and safeguards for actions taken against external systems. - Strong data and measurement judgment: experience using instrumentation and experiments to evaluate product outcomes, and the ability to reason about noisy data, delayed feedback, uncertainty, and correlation versus causation. - Sound judgment around systems that act on a user’s behalf, including authentication, authorization, sensitive data, approval boundaries, and limits on consequential actions. - Demonstrated ability to align cross-functional teams, explain technical trade-offs, mentor engineers, and drive execution without relying on reporting authority. - A product-minded approach to simplifying complex workflows for non-experts, with hands-on ownership from prototype through reliable production operation. PREFERRED QUALIFICATIONS - Experience building advertiser-facing products, marketing automation, campaign-management tools, growth platforms, or software that helps businesses acquire customers. - Experience integrating advertising or marketing APIs; familiarity with account onboarding, campaign lifecycles, and platform requirements. - Working knowledge of conversion tracking, attribution, customer acquisition cost, return on ad spend, budget pacing, and the limits of optimizing with low conversion volume. - Experience with experimentation, incrementality measurement, constrained optimization, recommendation systems, or decision-making under uncertainty. - Experience building permissioned, multi-tenant products with approval workflows, budget controls, audit trails, and privacy-aware handling of customer and conversion data. BONUS POINTS: - Experience with agent tooling, durable workflow execution, delegated authorization, or evaluation frameworks for agents that take consequential actions. - Experience connecting application events or server-side conversion signals to advertising platforms, or integrating creative generation and landing-page experimentation into growth workflows. - Experience building for small businesses or first-time advertisers, where clear guidance, limited budgets, and sparse data shape the product. - These are additional strengths, not a checklist. Experience with every ad platform is not expected; ad-serving infrastructure experience and an ML research background are not prerequisites. WHAT WE VALUE: - Customer outcomes: success means helping a business acquire qualified leads and customers—not merely connecting an account, launching a campaign, or increasing spend. - User control and trust: agents explain recommendations, act within explicit authority, and make budgets, costs, and consequential changes understandable. - Evidence over activity: we validate measurement, acknowledge uncertainty, and test whether a change improves business outcomes rather than treating attribution as proof of causality. - End-to-end ownership: we own the journey from a business goal through campaign execution, measurement, and ongoing improvement. - Pragmatic technical leadership: we build on existing ad platforms, ship focused increments, and invest in correctness and extensibility where they matter. - Clear collaboration: we document decisions, surface risks early, and help other teams and partners succeed. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

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Data Scientist, Product

Negotiable

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role: We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI. To realize this vision, we need people who are obsessed with understanding how users experience our product and can turn that understanding into decisions that move the needle. You'll directly impact Replit's growth by turning user behavior into actionable insights that shape product strategy, improve activation and retention, and drive sustainable revenue growth across our self-serve and enterprise segments. Who You are You're a data scientist who moves fast and goes deep. You can spin up an analysis in hours that would take others days; not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly. You're the person who digs past the top-line number to find the confound, questions whether the metric actually measures what people think it does, and pressure-tests your own work before anyone else sees it. You treat experimentation as a craft, not a checkbox. You've felt the pain of underpowered tests and novelty effects, and you have the judgment to get it right. You use AI agents and tools aggressively to multiply your output - writing code, exploring data, generating hypotheses, but you treat every AI-assisted output as a draft, not a deliverable. You know what good analysis looks like and you won't ship anything that doesn't meet that bar. The result is that you operate at a speed and depth that most DS teams can't match. YOU WILL: - Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical methodology. - Own the analytics for core product areas — Growth (activation, engagement, monetization & retention), feature adoption, product quality, AI agent effectiveness, and proactively surface insights that influence product roadmap decisions. - Build the analytical foundation for Replit's enterprise business, understanding team adoption patterns, workspace collaboration dynamics, and expansion signals that inform go-to-market and product strategy. - Develop predictive models to forecast frameworks to measure impact of features and new launches on user behavior, including likelihood to retain, convert, or expand, and embed those signals directly into product and growth workflows. EXAMPLES OF WHAT YOU COULD DO: - Design and analyze a complex multi-variant experiment testing pricing, onboarding, and feature gating simultaneously, navigating interaction effects and providing clear recommendations despite ambiguity. - Model the "aha moment" for new Replit users — identifying early behaviors most predictive of long-term retention — and work with the product team to redesign onboarding around those signals. - Analyze feature adoption and cohort behavior across user segments (students, hobbyists, professional developers, enterprise teams) to help PMs prioritize the roadmap with confidence. - Analyze enterprise team adoption patterns to identify what drives successful rollouts versus stalled deployments, and build the data models that power Replit's enterprise expansion playbook. REQUIRED SKILLS AND EXPERIENCE: - Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles. - 5+ years of experience in data science with a focus on product analytics, growth, or user behavior. - Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt. - Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.). - Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference. - You leverage AI tools extensively in your own analytical workflow and can demonstrate how they make you more effective, while maintaining high standards for output quality. PREFERRED QUALIFICATIONS: - Experience at a PLG company with a self-serve funnel and freemium or usage-based pricing model. - Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.). - Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching). - Experience designing ETL workflows and data pipelines using dbt or similar tools. BONUS POINTS: - You've built or contributed to AI-powered analytical tools, automation, or novel measurement approaches. - Experience analyzing freemium or usage-based pricing models. - Understanding of developer tools, collaborative coding environments, or technical products. - Experience working directly embedded with product teams in an agile environment. - Familiarity with customer data platforms (CDPs) and event tracking implementation. This is a full-time role that can be held from our Foster City, CA office. The role has an in-office requirement of Monday, Wednesday, and Friday. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

R

Research Program Manager - Model Evals and Safety

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ABOUT THE ROLE Research Program Managers at Reflection are high-leverage leaders and operators who embed directly with research and infrastructure teams to accelerate the pace of frontier model development. They are not project trackers. They are force multipliers who bring clarity to ambiguity, drive decisions when the path forward is unclear, and ensure that the work happening across multiple teams connects into a coherent whole. This is a foundational role. Reflection is building model evals and safety from the ground up, and this RPM will be at the center of that effort. You won't be stepping into an established function with existing processes and tooling. You will be the person who figures out what this function needs to look like, stands it up, and makes it real. That means defining the evaluation frameworks, building the operational infrastructure for model safety, establishing the processes that connect evals to the model development lifecycle, and laying the groundwork for how Reflection interfaces with the broader safety ecosystem. This is 0-to-1 work in its purest form. You bring a first-responder mentality. When things go sideways, you don't wait to be asked. You jump in, assess the situation, cut through noise, align the people who need to be aligned, and drive resolution. WHAT YOU'LL DO - Build the foundational infrastructure for model evals and safety at Reflection. Define the evaluation frameworks, tooling requirements, and operational processes that will underpin how we assess model capabilities, risks, and readiness for release. - Stand up model safety operations as a function, including establishing the workflows, review cadences, and decision frameworks that connect safety evaluation to the model development and release lifecycle. - Partner with research and engineering leads across pre-training, mid-training, and post-training to embed safety and evaluation checkpoints into the development process in a way that is rigorous without being a bottleneck. - Drive the scoping and prioritization of eval science and eval infrastructure investments, working with technical leads to determine what to build in-house, what to adopt, and where to invest research effort. - Establish Reflection's engagement with the external safety ecosystem, including third-party assessments, academic partnerships, and industry safety frameworks. Represent the company's safety posture to external stakeholders with credibility and clarity. - Create visibility and reporting structures that give leadership a clear, honest picture of model safety status, evaluation coverage, and open risks, so they can make informed decisions at the pace the business requires. - Champion a culture of blameless post-mortems and continuous learning, turning every safety-relevant finding into a concrete improvement to our systems and processes. ABOUT YOU - 7+ years of experience in technical program management, research operations, or ML engineering, with demonstrated experience standing up new functions, teams, or programs from scratch. - Familiar with the landscape of model evaluation and AI safety, including evaluation methodologies, red-teaming, alignment research, and the evolving regulatory and industry safety ecosystem. You don't need to be a safety researcher, but you need to understand the space well enough to make sound judgments about what matters and what to prioritize. - Deep enough technically to engage with researchers and engineers on topics like model behavior, evaluation design, data pipelines, and safety-critical system architecture. You follow the technical thread and you know when something doesn't add up. - Proven ability to build structures where none exists. You've taken ambiguous mandates and turned them into functioning programs with clear ownership, measurable outcomes, and durable processes. - Strong stakeholder management skills spanning deeply technical ICs, research leadership, and external partners. You build trust through competence and follow-through. - Excited to build from zero to one. We are a small, fast-moving team and this role will help define how model safety and evaluation works at Reflection. - Motivated by enabling researchers and engineers to build the world's most capable open-weight AI systems, responsibly. WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Member of Technical Staff, Data Flywheel

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ROLE OVERVIEW The Data Flywheel team closes the gap between benchmark performance and useful performance in the real world. We identify and build the signals, data, and feedback loops that turn model usage into rigorous evaluations, targeted training data, and measurable improvements in future generations of models. This is a hands-on technical role at the intersection of research and deployment. You'll take ambiguous model behaviors from first observation through measurement, intervention, and validated improvement, working across evaluation, human and synthetic data, infrastructure, post-training, and live deployments. You'll collaborate closely with researchers and engineers across the company, as well as customers, partners, vendors, and the open-source community. WHAT YOU’LL DO - Identify high-value data sources and partnership opportunities, deeply understand the underlying use cases, and translate them into representative evaluations - Bring new data sources online, from initial partner conversations and data scoping through quality validation and integration into production evaluation and training pipelines, where appropriate - Design and build evaluations, graders, and feedback loops that make priority real-world model behaviors measurable - Analyze model performance and failure modes, then translate those insights into targeted datasets, reward signals, and training interventions - Develop human and synthetic data strategies for capabilities where existing data is insufficient, including designing and running evaluation and data-collection programs with vendors - Build the infrastructure and pipelines needed to ingest, inspect, version, and evaluate data reliably at scale - Collaborate closely with pre-training, post-training, applied, and partnership teams to turn new signals into measurable model improvements WHAT WE’RE LOOKING FOR - Degree (BS, MS, or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience - Deep technical understanding of LLM training and evaluation, with hands-on experience in areas such as evaluation design, data curation, reinforcement learning, or reward design - Strong software engineering skills and experience building automated data/evaluation pipelines or large-scale ML systems - A track record of owning high-impact projects end to end, navigating ambiguity, and adapting quickly as priorities change - A highly collaborative, action-oriented approach and excitement about defining how a new frontier lab measures and accelerates model progress - High agency and thrive in a fast-paced startup environment; bias for impact over process - Enjoy collaborating across research, engineering, operations, and product disciplines WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Member of Technical Staff - Safety

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ABOUT THE ROLE - Own the red-teaming and adversarial evaluation pipeline for Reflection’s models, continuously probing for failure modes across security, misuse, and alignment gaps. - Work hand-in-hand with the Alignment team to translate safety findings into concrete guardrails, ensuring models behave reliably under stress and adhere to deployment policies. - Validate that every release meets the lab’s risk thresholds before it ships, serving as a critical gatekeeper for our open weight releases. - Develop scalable, automated safety benchmarks that evolve alongside our model capabilities, moving beyond static datasets to dynamic adversarial testing. - Research and implement state-of-the-art jailbreaking techniques and defenses to stay ahead of potential vulnerabilities in the wild. ABOUT YOU - Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience in AI Safety. - Deep technical understanding of LLM safety, including adversarial attacks, red-teaming methodologies, and interpretability. - Strong software engineering capabilities with experience building automated evaluation pipelines or large-scale ML systems. - Experience with Reinforcement Learning (RLHF/RLAIF) and how it impacts model safety and alignment is a strong plus. - Thrive in a fast-paced, high-agency startup environment with bias toward action. - Willing to make high-stakes decisions regarding model release and safety thresholds. - Passionate about advancing the frontier of intelligence. WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Member of Technical Staff - Research Software Engi...

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. THE ROLES MISSION Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models — from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines. Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility. WHAT THIS TEAM DOES This team owns and evolves the core infrastructure behind our training systems. We focus on: - Reinforcement learning training infrastructure - Distributed training and inference systems - Experiment infrastructure and reproducibility - Large-scale data pipelines The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale. ABOUT THE ROLE You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines. You will work closely with researchers to transform new ideas into reliable, scalable training systems. Responsibilities include: - Designing and optimizing large-scale training loops and data pipelines. - Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient. - Building internal tooling for launching, monitoring, and reproducing complex experiments. - Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls). - Translating research prototypes into reusable, production-grade infrastructure. WHAT YOU'LL WORK WITH Distributed Training - GPU parallelism (data, tensor, pipeline, expert) - Large-scale distributed training infrastructure - Communication optimization (NCCL, RDMA, GPU interconnects) - FSDP / ZeRO and model sharding Orchestration & Runtime Systems - Ray, Kubernetes, Slurm - Distributed runtimes and async systems - Containerization and sandboxing Frameworks - PyTorch - JAX - Megatron-style training stacks - Triton / custom kernels Data Infrastructure - Large-scale dataset curation pipelines - Deduplication and filtering systems - Tokenization and preprocessing - Distributed data processing frameworks ABOUT YOU - You are a strong software engineer who speaks the language of machine learning. - You may not have a PhD, but you know how to implement a research paper. - You have deep experience in at least one of the following: Distributed Training & Inference or Data Infrastructure - You enjoy working at the boundary between: - Machine learning algorithms - Distributed systems - High-performance computing - You care deeply about performance, numerical stability, and reproducibility. - You thrive in high-agency environments and enjoy solving hard technical problems. WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Member of Technical Staff - Pre-Training Infra

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ABOUT THE ROLE - Build and scale distributed training systems that power frontier model pre-training. - Work closely with research teams to design and operate large-scale training runs for foundation models. - Develop infrastructure that enables efficient training across thousands of GPUs using modern distributed training frameworks. - Optimize training throughput, stability, and efficiency for large model training workloads. - Collaborate directly with pre-training researchers to translate experimental ideas into scalable, production-ready training systems. - Improve performance of distributed training workloads through optimization of communication, memory usage, and GPU utilization. - Build and maintain training pipelines that support large-scale datasets, checkpointing, and experiment iteration. - Debug and resolve performance bottlenecks across distributed training stacks including model parallelism, GPU communication, and training runtime systems. - Contribute to the development of systems that enable rapid experimentation and iteration on new training techniques. IDEAL EXPERIENCE - Experience building or operating distributed training systems for large machine learning models. - Strong experience working with modern distributed training frameworks such as Megatron, DeepSpeed, or similar large-scale training systems. - Familiarity with large-scale model parallelism strategies (data, tensor, pipeline, or expert parallelism). - Experience optimizing training throughput and GPU utilization in large distributed environments. - Familiarity with GPU communication libraries such as NCCL and performance tuning for distributed workloads. - Experience working closely with ML researchers to productionize experimental training workflows. - Strong debugging skills across GPU compute, distributed training systems, and large-scale ML pipelines - Experience working with large datasets and training pipelines used for foundation model pre-training. WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Member of Technical Staff - Data Quality Engineer...

Negotiable

OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ABOUT THE ROLE Data is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data. As a member of the Data Team, your mission is to ensure that the data used to train our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities. Working with world-class researchers on our pre-training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model performance. Working closely with our pre-training teams you will: - Own upstream data quality for LLM pre-training; as a specialist or generalist across languages and modalities - Partner closely with research and pre-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors - In addition to human-in-the-loop processes, you will design, validate, and scale automated QA methods to reliably measure data quality across large campaigns - Build reusable QA pipelines that reliably deliver high-quality data to pre-training teams for model training - Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria ABOUT YOU - Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre-training data - Detail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality - Solid understanding of how data quality impacts pre-training, with the ability to translate quality concerns into concrete signals, decisions, and feedback - Experience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-Judge - Comfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partners SKILLS AND QUALIFICATIONS - Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code - Experience working with large datasets and automated evaluation or quality-checking systems - Familiarity with how LLMs work and can describe how models are trained and evaluated - Excellent communication skills with the ability to clearly articulate complex technical concepts across teams WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf

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Forward Deployed AI Strategy Lead

Negotiable

FORWARD DEPLOYED AI STRATEGY LEAD OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. THE ROLE The most important AI products of the next decade will not be built by simply renting GPUs or calling an API. They will be built by teams that can define the right tasks, construct the right environments, measure the right outcomes, run the right post-training loops, and deploy models that improve on real workflows. Prime Intellect gives customers that capability. Your job is to make it real. As a Forward Deployed AI Strategy Lead, you will work directly with strategic customers to identify high-value AI workflows, translate them into evals and post-training opportunities, scope technical deployments with Applied Research, and turn early experiments into long-term revenue. You are part customer owner, part product strategist, part AI systems thinker, and part commercial operator. You will not sit between the customer and the technical team as a messenger. You will sit with both sides and help invent the answer. WHAT YOU’LL DO OWN STRATEGIC CUSTOMER DEPLOYMENTS You will lead high-priority customer workstreams from first technical discovery through POC, deployment, expansion, and case study. You will work with customers who are trying to build agents, automate complex workflows, improve model performance, reduce inference cost, build domain-specific evals, or run frontier-scale post-training. You will help them answer: - What should we train or evaluate? - What does success actually mean? - What workflows are worth turning into environments? - What data or traces are needed? - What should be automated, supervised, or measured? - Which model should be adapted? - What is the path from prototype to production? TURN AMBIGUITY INTO SCOPE Customers rarely arrive with a perfectly defined problem. You will take messy conversations, scattered artifacts, internal docs, product goals, and technical constraints, and turn them into crisp scopes that Applied Research and Engineering can actually execute. You will define: - Use cases - Success metrics - Eval design - Environment requirements - Integration needs - Milestones - Commercial structure - Risks and dependencies - Expansion path PARTNER DEEPLY WITH APPLIED RESEARCH This role works hand-in-hand with Applied Research. You will bring customer signal into the research and product roadmap, helping the team identify which evals, environments, agents, and post-training recipes matter most in the field. You will help prioritize work that can both advance the frontier and unlock meaningful customer outcomes. You should be excited to spend time around questions like: - How do we convert real-world workflows into reliable RL environments? - What makes an eval useful instead of decorative? - When is a verifier good enough? - What makes a task trainable? - Where does managed RL outperform prompting or manual workflow design? - How do we prove performance improvement to a skeptical customer? BUILD THE REPEATABLE MOTION Every strategic deployment should make the next one easier. You will help build the operating system for Prime Intellect’s applied AI motion: - Discovery templates - Customer qualification frameworks - POC structures - Proposal language - Pricing and packaging inputs - Reference architectures - Case studies - Technical narratives - Deployment playbooks You will help turn one-off customer wins into a repeatable category. DRIVE REVENUE This is a customer-facing role with real revenue responsibility. You will work with leadership to move customers through qualification, legal, scoping, proposal, procurement, POC, deployment, and expansion. You should be comfortable owning senior customer relationships, creating urgency, writing crisp follow-ups, navigating internal and external stakeholders, and making sure important deals do not die in ambiguity. WHAT WE’RE LOOKING FOR We are looking for people who are unusually strong across technical understanding, customer empathy, product judgment, and execution. You might be a strong fit if you have experience in: - Forward deployed engineering or technical GTM - AI product strategy or applied AI - Solutions architecture for highly technical products - Early-stage startup operating roles - Product management for AI, infra, devtools, or enterprise software - ML engineering, applied research, or AI engineering with customer exposure - Venture/investing roles with deep technical and commercial work in AI You should have: - Strong intuition for AI products and workflows - Ability to understand technical systems without needing every detail pre-digested - Excellent written and verbal communication - Comfort operating with executives, researchers, engineers, and operators - High agency and low ego - Ability to run multiple complex customer workstreams - Taste for what makes a deployment valuable - Strong commercial instincts - Deep curiosity about post-training, agents, evals, RL, and AI infrastructure - Ability to make progress before the playbook exists BONUS POINTS - Experience with RL, SFT, evals, agents, MCP, LangGraph, DSPy, Stagehand, Browserbase, or tool-use workflows - Experience working with enterprise AI teams or frontier AI companies - Ability to read traces, product docs, API docs, or technical specs and turn them into a deployment plan - Experience writing proposals, customer memos, technical scopes, or launch narratives - Founder or early startup experience - Strong network across AI startups, research labs, or enterprise software buyers WHY THIS ROLE This is one of the highest-leverage roles at Prime Intellect. The frontier is moving from models to systems: agents, environments, evals, training loops, and deployment infrastructure. Most companies know they need to adapt models to their own workflows, but they do not know how to turn that ambition into a working system. You will be the person who helps them get there. You will work on customer problems that are technically real, commercially urgent, and strategically important. You will help shape the product, close the revenue, and define the emerging category of full-stack post-training infrastructure. This is a role for builders who want to be close to the frontier and close to the market. WHAT WE OFFER - Competitive cash compensation and meaningful equity - Flexible work in San Francisco or hybrid-remote - Visa sponsorship and relocation support - Professional development budget - Team off-sites and conference attendance - Direct exposure to frontier AI labs, leading AI startups, and enterprise AI teams - A rare opportunity to help define how the next generation of AI systems are trained, evaluated, and deployed READY TO BUILD THE INTERFACE BETWEEN FRONTIER AI AND THE REAL WORLD? Apply to help Prime Intellect turn ambitious customer workflows into post-training systems, revenue, and the foundation for open superintelligence.

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Senior Machine Learning Software Engineer, Researc...

Negotiable

About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Note: We are currently recruiting for multiple positions across different levels, however please only apply for the role that best aligns with your skillset and career goals. What you will do - Own the training stack end to end: experiment harness, sweeps, distributed training, checkpointing, and throughput. - Implement and optimise model architectures with efficiency perspective: sparse attention and convolution, custom kernels, memory and precision work, and making a prototype fast enough to train at scale. - Take architectural ideas from prototype to trained model, and own the ablations that establish whether a change actually helped. - Set technical direction for the training stack, and build the inference and evaluation path that turns a checkpoint into something usable. - Identify engineering investments that unlock research progress. What you bring to the table - Senior-level experience in training machine learning models at scale, in industry or a research group of comparable intensity, instrumental in: - distributed training with FSDP or equivalent, including profiling and diagnosing idle GPUs; - PyTorch and CUDA at depth, ideally including sparse convolution or sparse attention internals and custom CUDA or Triton kernels; - implementing architectures from papers and making them work, then making them fast; - reproducible experiment infrastructure, because most of the compute in a programme like this is spent on runs that fail. - Experience with 3D or other irregular data: meshes, point clouds, voxels, sparse tensors. - Ability to scope and deliver projects, and strong problem-solving skills. - Degree in CS, engineering, mathematics, physics or a related field. A PhD is welcome but not required; engineering depth matters more. - Desirable: open-source contributions, public technical writing, or papers at MLSys, SIGGRAPH or CVPR What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow… We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

P

Machine Learning Engineer

Negotiable

About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used. You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. Note: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license. This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes. What you will do - Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving - Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable - Explore and manipulate 3D point cloud & mesh data - Own the delivery of technical workstreams - Create analytics environments and resources in the cloud or on premise, spanning data engineering and science - Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success - Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products - Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter , where you'll collaborate closely with customers to build solutions on-site. What you bring to the table - Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings. - Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged. - A track record of scoping and delivering projects in a customer facing role - 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) - Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) - Distributed computing frameworks (e.g., Spark, Dask) - Cloud platforms (e.g., AWS, Azure, GCP) and HP computing - Containerization and orchestration (Docker, Kubernetes) - Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly - Excellent collaboration and communication skills - with teams and customers alike - A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan. 🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. ☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 📈 Personal development – dedicated support for learning, development, and leveling up over time. 💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing. 💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses. 🔎 Watch this space, we’re continuing to build this as we grow… Salary range: $150,000 - $190,000 depending on experience Seniority will be assessed throughout our interview process We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

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Data Scientist

Negotiable

About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Who We're Looking For As a Data Scientist in Delivery, you are a problem solver and builder who is passionate about creating practical solutions that enable customers to make better engineering decisions. You are someone who can grasp advanced engineering concepts across multiple industries, and you excel at working directly with customers (and often side-by-side with them on-site) to transform cutting edge AI models into tools that are useful and used. You’ve worked on difficult problems that require strong foundations in data driven modelling and deep learning techniques, with hands-on experience in probabilistic methods and predictive modelling. Expertise in python, along with proficiency in libraries like NumPy, SciPy, Pandas, TensorFlow and PyTorch, is essential, with the ability to deploy scalable, production-ready models and data pipelines. With at least 1 year industry experience (post Masters or PhD) in a commercial, non-research environment, you’re ready to hit the ground running. You’re truly excited about growing your technical expertise and are naturally inclined to take ownership of data science work streams, continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. Note: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license. This Role In this role, you’ll work closely with our Simulation Engineers, Machine Learning Engineers, and customers to understand and define the engineering and physics challenges we are solving. You’ll build the foundations for successful, impactful solutions by: - Pre-processing and analyzing data to prepare it for use in predictive modelling, building the foundation for machine learning algorithms to be developed. - Developing and utilizing innovative deep learning models in combination with state-of-the-art optimization methods to predict and control the behaviour of physical systems. - Taking full responsibility for the quality, accuracy and impact of your work. - Designing, building and testing data pipelines that are reliable, scalable and easily deployable in production environments. - Working closely with simulation engineers to ensure seamless integration of data science models with simulations. - Contributing to internal R&D and product development, helping to refine models and identify new areas of application. - Engaging in open communication and presentation with both technical teams and customers, helping onboard users and co-develop with customers. You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter , where you'll collaborate closely with customers to build solutions on-site. As the role evolves, there are exciting opportunities for growth as an individual contributor or a technical lead, especially if you’re driven by taking ownership of more complex projects and leading the direction of future solutions. Please note, this role is based in Manhattan, NYC, working 2-3 days per week in our office. Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan. 🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. ☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 📈 Personal development – dedicated support for learning, development, and leveling up over time. 💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing. 💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses. 🔎 Watch this space, we’re continuing to build this as we grow… Salary Range $120,000 - 240,000 depending on experience Seniority will be assessed throughout our interview process We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

P

ML Infra Engineer, Data Systems

Negotiable

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future. As an ML Infra Engineer (Data Systems), you’ll build and operate the data infrastructure that powers large-scale robot learning. Your systems will sit directly between raw data sources and training/evaluation, enabling us to move faster while maintaining performance, correctness, and reliability at scale. This is a systems role at the intersection of distributed systems, storage, and machine learning infrastructure. The Team The Infrastructure organization builds the foundations that make large-scale learning possible at PI. This includes training systems, data platforms, evaluation pipelines, and the tooling that allows researchers and roboticists to work with massive datasets safely and efficiently. In This Role You Will - Data Ingestion & Processing: Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data. - Batch & Streaming Systems: Operate large-scale batch and streaming workflows over massive datasets. - Storage Systems: Design object storage layouts, metadata systems, and efficient access patterns; choose file formats with performance and scalability in mind. - Data Lifecycle Management: Build systems for backfills, dataset rebuilds, garbage collection, and large-scale transformations. - Training-Time Performance: Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival → model training. - Metadata & Indexing: Build scalable metadata stores for datasets, annotations, and training artifacts. - Data Movement: Move petabytes efficiently across clusters and environments. - Operational Correctness: Implement observability, validation, and guardrails to prevent silent data regressions. - Cross-Functional Collaboration: Work closely with cross-functional teams of researchers, engineers and roboticists to translate evolving data needs into robust systems. What We Hope You’ll Bring - Strong software engineering fundamentals. - Experience building distributed systems or large-scale data pipelines. - Comfort reasoning about performance, memory, I/O, and storage efficiency. - Familiarity with batch and/or streaming processing systems. - Experience with object storage systems and data format tradeoffs. - Ownership mindset: design, build, operate, and iterate on systems end-to-end. - Enjoy working closely with researchers and unblocking fast-moving projects. Bonus Points If You Have - Experience with large ML training pipelines or dataloading systems. - Knowledge of columnar or custom data formats. - Experience with systems like ClickHouse, Ray, Flink, Spark, or similar. - Hands-on experience operating petabyte-scale datasets. - Debugging and fixing performance bottlenecks in data-heavy systems. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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Research Scientist/Research Engineer, Midtraining

Negotiable

We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and a drive to push the boundaries of what's scientifically possible. ABOUT THE ROLE We're training frontier models to develop deep scientific knowledge and reasoning for scientific discovery. As a Midtraining Research Engineer, you'll take base models and improve their scientific reasoning: curating and generating data, building evals, and running large-scale training experiments. Your work will also lay the groundwork for our pre-training efforts down the line. WHAT YOU'LL DO - Identify, process, and curate novel sources of scientific data for large-scale model training. - Generate high-quality synthetic data to fill gaps in scientific knowledge and reasoning. - Build evaluations that correlate with downstream scientific task performance, working closely with RL researchers, physicists, and chemists. - Develop and apply techniques such as self-distillation and on-policy distillation to improve model capability. - Design and run large-scale training experiments, partnering with supercompute engineers to scale efficiently across thousands of GPUs. - Build tools for yourself and the team to investigate how data choices shape model intelligence. YOU WILL THRIVE IN THIS ROLE IF YOU HAVE - Experience training LLMs on curated mixes of trillions of tokens. - Experience on a dedicated evals team supporting a large production training run. - Hands-on use of self-distillation, on-policy distillation, or similar methods in a real training pipeline. - Experience with scaling laws and compute-optimal hyperparameters. - Comfort working across data, evals, and training infrastructure. ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE - Experience optimizing throughput and reliability for large-scale distributed training runs. - A background in AI for science or training on specialized domain data (e.g., protein, materials, or other scientific datasets). - Experience creating evals or synthetic data for non verifiable tasks and tracking performance over live runs. MECHANICS - Minimum education: Bachelor's degree or similar experience - Location: Menlo Park, CA - Compensation: $250,000–$350,000 + equity - Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.

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Staff Product Manager, AI Product

Negotiable

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes. PathAI is looking for an experienced, strategic Staff AI Product Manager to help shape and execute our core product roadmap. As a Staff PM, you will serve as a strategic leader and cross-functional connector, partnering closely with machine learning, pathology, biomedical data science, clinical affairs, quality, regulatory, and business operations teams. You will be responsible for defining multi-year product visions, building business cases, and driving high-impact AI/ML algorithms from initial scientific discovery to deployment. This role offers a high-visibility opportunity to lead zero-to-one product initiatives and scale existing algorithm portfolios in a fast-growing, dynamic health-tech environment. The Opportunity: - Drive Strategic AI Product Vision: Define and execute the multi-year product strategy and roadmap for PathAI algorithm products within a key business unit or disease area (e.g., oncology). - Lead End-to-End Product Lifecycle: Conduct product explorations, build business cases, define requirements, oversee prototyping, and lead the cross-functional deployment of new AI products and product line extensions. - Cross-Functional Leadership: Partner closely with AI/ML engineers, data scientists, clinical specialists, product designers, and pathologists to translate complex algorithmic discoveries into scalable, user-centric clinical and research tools. - Commercial & Scientific Alignment: Build compelling business cases to motivate the development of novel technical, scientific, and platform capabilities for PathAI. - Regulatory & Quality Integration: Collaborate with Clinical Affairs, Quality, and Regulatory teams to support validation strategies (including SaMD considerations) for clinical trials and regulatory pathways. Who You Are: (Required) - You have a bachelor’s or advanced degree in Computer Science, Computational Biology, Biomedical Engineering, Biology, or a related technical/scientific field. - You have 7+ years of experience in product management, with a track record of launching and scaling production AI/ML, data science, or bioinformatics products. - You have demonstrated experience driving product development within healthcare, life sciences, digital pathology, or biotech domains. - You have a demonstrated understanding of the end-to-end AI/ML product lifecycle, including model development, dataset curation, evaluation metrics, and monitoring algorithms post-launch. - You have exceptional cross-functional collaboration and communication skills, with a demonstrated ability to influence stakeholders across technical, scientific, clinical, and executive teams. Preferred Qualifications: - You have experience with Software as a Medical Device (SaMD) and familiarity with regulatory frameworks (e.g., FDA 510(k)/PMA pathways, CE-IVD). - You are a subject matter expertise or background in oncology, histopathology, or biomarker discovery. - You have experience building products for translational research, biopharma partnerships, or clinical trial operations. - You have excellent interpersonal and communication skills, with a proven ability to collaborate effectively within and across teams. - You have excellent organizational skills, including the ability to efficiently evaluate, prioritize and handle multiple changing projects and priorities; you complete work in a timely, accurate and thorough manner This position is based in Boston, MA (preferred) or NYC. Relocation benefits are not available for this position. The expected salary range for this position based on the primary location Boston, MA is $146,250 - $224,250. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. PathAI is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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Solutions Architect, Industrial Robotics

Negotiable

We are looking for a Solutions Architect, Industrial Robotics to engage with customers, partners, and other NVIDIA software engineers to support applications and platforms that leverage NVIDIA technology that will be adopted across all industry segments. At NVIDIA, our Solutions Architects are drawn from elite developers and scientists who enjoy working with the latest in GPU and Networking hardware and software! NVIDIA’s platforms for machine learning, deep learning and high-performance computing have already made a major impact and are broadly used across leading North America companies. Our success has been based on vertical focus industry business development, building a strong developer ecosystem, and scaling out the business using a broad partner channel including HW OEMs, Cloud Service Providers, Global System Integrators, and VARs. A Solutions Architect is the bridge of technical expertise between our industry teams, our partners and our customers. Dynamically engaging with internal developers, scientific researchers, data scientists, IT managers, and senior leaders is an important part of the Solutions Architect role, and will give you experience with a range of partners and concerns. What you'll be doing: Partner with our industry and account teams to develop a keen understanding of customer goals, strategies, and technical needs as well as help to define and deliver high-value solutions meeting these needs. Document what you know and teach others. This can vary from building targeted samples, to writing whitepapers, blogs, and wiki articles, to simply working through hard problems with a customer on a whiteboard. Be an industry leader with vision on integrating NVIDIA technology into Manufacturing use cases with AI, HPC, and enterprise GPU and networking architectures for advanced applications both in the datacenter and at the edge. Strategically partner with lighthouse customers and industry-specific solution partners targeting our computing platform. What we need to see: BS, MS, or PhD in Robotics, Engineering, Computer Science or equivalent experience. 5+ years of work-related experience in robotics, humanoids and AI domains - e.g. VLA, VLM, Transformers, Synthetic Data Generation, software development with knowledge of parallel computing with GPUs. Background working with modern application deployment practices, including but not limited to Docker/Containers and Kubernetes. Experience with modern Deep Learning software architecture and frameworks. Strong written and oral communication skills with the ability to effectively collaborate with management and engineering. Experience supporting customers/partners in technical engagements. Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very complex projects. Strong analytical and problem-solving skills. Ways to stand out from the crowd: Experience applying AI to robotics and humanoid use cases, such as VLA, VLM training, simulation, and digital twins. Experience with frameworks like Tensorflow, Pytorch, TRT, and models like Vision Transformer. Background with NVIDIA edge lineup including Jetson, Thor, and other platform toolings. Experience using GPU programming, design practices and profiling tools like Nsight systems. AI knowledge and expertise: Deep Neural Network research and development experience with specialized domains. Machine Learning research and development experience with industry data OR published record of thought leadership in a technical area of simulation, robotics, data generation, AI. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, results-oriented and enjoy learning while having fun, then what are you waiting for? Apply today! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 6, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

N

Software Engineering Intern, Test Development - 20...

Negotiable

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. What you’ll be doing: Test NVIDIA AI agents and end-to-end agentic workflows across NV GPU Platform. Design and execute functional, integration, end-to-end, reliability, and performance tests. Develop automated tests and evaluation frameworks for capabilities such as reasoning, tool use, multi-turn interactions, memory, and retrieval. Use AI development tools to improve productivity across test planning, test case development, execution, analysis, and automation. Collaborate with development teams to triage issues, perform root-cause analysis, verify fixes, and improve test coverage and quality. What we need to see: Currently pursuing a master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Strong software development potential, with proficiency in Python and shell scripting. Familiarity with AI, machine learning, large language models, or AI agent technologies. Experience using AI development tools to improve quality and productivity across the end-to-end QA workflow. Good knowledge of Linux and software testing methodologies. Strong problem-solving, communication, and interpersonal skills. Fluent written and spoken English. A proactive, innovative, and self-motivated attitude with a passion for learning cutting-edge technologies. Ability to work full-time, on-site five days per week, for one year. Ways to stand out from the crowd: Experience testing LLM applications, AI agents, or agentic workflows. Experience developing AI evaluation or test automation frameworks. Knowledge of RAG, tool calling, prompt engineering, or multi-agent systems. Experience with Docker, Kubernetes, OpenStack, or other cloud-native technologies. Experience with NVIDIA GPU hardware, CUDA, or parallel programming.