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Machine Learning Engineer - Content Discovery

Negotiable

ABOUT SUNO We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfillment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world. Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible. Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA). About the Role We’re looking for early members of our machine learning recommendations team. You’ll work closely with the founding team and have ownership of a wide variety of technical decisions on how we build and deploy our state of the art recommendation models. Check out the Suno version of the job here! https://suno.com/s/ujpqmS3Rf9Qg911A What You’ll Do - Formulate and develop mathematical models of user preference, similarity, and engagement for music discovery - Design learning systems that infer user taste from sparse, noisy, and evolving interaction data - Build and deploy scalable recommendation and ranking models that operate under real-time latency and throughput constraints - Translate abstract objectives (relevance, novelty, diversity, long-term satisfaction) into measurable metrics and optimized systems - Run large-scale experiments and causal analyses to evaluate model behavior and product impact - Work closely with product and research leadership to define the technical direction of Suno’s personalization systems What You’ll Need - Strong background in applied mathematics, statistics, machine learning, or a related quantitative field (PhD or equivalent experience) - Experience designing models from first principles (e.g., probabilistic models, optimization-based systems, representation learning, graph-based methods) - Proficiency in Python and modern ML frameworks (e.g., PyTorch) with the ability to implement and iterate on research ideas - Familiarity with learning from user interaction data (implicit feedback, ranking losses, bandits, or reinforcement-learning-adjacent methods) - Comfort reasoning about tradeoffs between model quality, scalability, and system constraints - Curiosity, rigor, and a desire to understand systems deeply rather than treating models as black boxes - A love of music (listening, exploring, or making) is a strong plus Additional Notes: Applicants must be eligible to work in the US. Perks & Benefits for Full-Time Employees - Company Equity Package - 401(k) with 3% Employer Match & Roth 401(k) - Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options) - 11 Paid Holidays + Unlimited PTO & Sick Time - 16 Weeks of Paid Parental Leave - Creative Education Stipend - Generous Commuter Allowance - In-Office Lunch (5 days per week) Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.

S

AI Product Engineer

Negotiable

ABOUT US Our mission is to reinvent the way people learn, starting with language. Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages. Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana. ABOUT THIS ROLE As an AI Product Engineer at Speak, you'll play a pivotal role in developing the future of language learning and creating the most effective path to language fluency. Your primary responsibility will be developing innovative product experiences powered by Language Learning Models (LLMs) and deploying them to millions of users. Your tasks will span the full stack and beyond, from evaluating models and testing prompts to developing and iterating on various product features such as our conversational onboarding and lesson experiences, grammatical assessment and feedback, and personalized lessons. We're constantly pushing the boundaries of what LLMs can do to provide an exceptional and unparalleled language learning experience to users in over 30 countries worldwide. WHAT YOU'LL BE DOING - Developing and deploying LLM-powered language learning products across the full stack, as well as enhancing the quality and performance of existing AI-powered features within Speak - Collaborating cross-functionally with other Engineering teams, Applied ML, Product, Design, and Content - Refining our process for building LLM apps, including best practices for prompting, experimentation/evaluation, LLM Ops, measuring quality and performance, etc. - Scaling existing product features to many more users and languages WHAT WE'RE LOOKING FOR - 3-5+ years of experience in full stack/backend, product-focused software engineering - Proficiency in React/Node/Typescript and Python - You have real-world experience developing and deploying LLM apps and a strong understanding, gained through experience, of what works and what doesn't - A keen intuition for improving performance and output quality of LLM systems - Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops) - Strong product intuition — the ability to think broadly and cross-functionally about innovative LLM-powered capabilities and product experiences - Ability to work independently and build at a high velocity OFFICE - San Francisco, CA WHY WORK AT SPEAK 1. Join a fantastic, tight-knit team at the right time: we're growing very quickly, we've most recently raised our Series C from some of the top investors in the valley, and we've achieved product-market fit in our initial markets. You'd join at a magical time when a single person could significantly change the course of the company. 2. Do your life's work with people you’ll love working with: we care strongly about our craft and want every person at Speak to feel like they're growing every day. We believe in the idea that working with people you both enjoy and have respect for makes everything better. We hire thoughtfully and only work with people we admire deeply. 3. Global in nature: We're live in over 40 countries and launching in a number of new markets soon. We have dedicated offices in San Francisco, Ljubljana, Seoul, Tokyo, and Taipei, and you’ll have the opportunity to talk to users in each of these regions on a regular basis as well as travel. 4. Impact people's lives in a major way: Learning a language is one of the single most life-changing skills one can learn, and right now 99% of people never achieve their goal because the process is broken. We’re helping millions of people achieve their goals and improve their lives. Speak does not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

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Manager, Forward Deployed Engineering

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! About the Role Snorkel AI is hiring a Manager, Forward Deployed Engineering to build and lead our Forward Deployed Engineering (FDE) function within the Data-as-a-Service (DaaS) organization. This team is responsible for the technical execution layer of DaaS delivery — owning the systems, workflows, and quality frameworks that power high-quality dataset production at scale. You will lead a team of engineers who operate across the full AI data lifecycle: designing evaluation systems, building ML-assisted workflows, and developing the technical foundations that make human-in-the-loop (HITL) data generation faster, more reliable, and scalable. This is a hands-on, player coach role where you’ll both help define the strategy and architecture of our data pipelines while staying close to implementation. As a founding leader, you will establish how FDE partners with Delivery, Engineering, and Research to ensure consistent, high-quality execution across all DaaS projects. You’ll operate at the intersection of data engineering, ML engineering, and production operations, driving the evolution of our technical delivery capabilities and enabling Snorkel to scale its impact across increasingly complex AI workloads. Main Responsibilities - Build and lead the Forward Deployed Engineering function, defining the team’s operating model, technical roadmap, and standards for execution - Hire, mentor, and develop a high-performing team, fostering strong ownership, velocity, and engineering excellence - Own the design and evolution of end-to-end AI data pipelines, including dataset generation, evaluation systems, and quality frameworks - Drive development of ML-assisted and HITL workflows that improve the speed, scalability, and reliability of data production - Establish and standardize measurement, benchmarking, and validation systems to ensure consistent, high-quality dataset delivery Develop scalable internal tooling and workflows for orchestration, monitoring, and delivery, reducing bottlenecks and improving throughput - Partner cross-functionally with Delivery, Engineering, and Research to productionize new approaches and continuously improve DaaS capabilities What We’re Looking For - 6+ years of experience in AI/ML/GenAI engineering, forward deployed engineering, solution engineering or applied AI roles, with strong hands-on technical depth - 2+ years of experience leading or managing engineers in a hands-on, player-coach capacity - Strong experience building and operating data pipelines and ML/LLM-based workflows in production environments - Proficiency in Python, SQL, and modern data tooling; experience integrating systems via APIs. Has written code in the last 2 years - Experience designing evaluation frameworks, quality measurement systems, or benchmarking approaches for ML systems - Strong systems thinking — ability to design scalable, reusable solutions rather than one-off implementations - Proven ability to operate in ambiguous, fast-paced environments and drive execution across cross-functional teams. Start-up or growth-stage experience is strongly preferred. - Customer-facing, consulting, or services experience is strongly preferred Nice to have: - Experience with human-in-the-loop (HITL) systems, data annotation workflows, or data-centric AI approaches. - Familiarity with synthetic data generation or model-assisted labeling techniques. - Experience building internal platforms or tooling for delivery or operations teams Why Join Snorkel AI? At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to find a function and have a broad, lasting impact across the company. Compensation The base salary range for this position is $200,000 – $320,000 . This range reflects multiple factors, including role level and work location. The final offer within this range will be determined based on job-related skills, experience, relevant education or training, interview performance, and other business considerations. Most offers include equity and benefits. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $200,000 - $320,000 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.

S

Head of Forward Deployed Engineering

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! About the Role In this role, you will build and lead our forward-deployed engineering (FDE) team, working directly with leading labs and enterprises to scope, build and deliver high quality datasets to support their most critical AI initiatives. You’ll lead a team that will own quality in the end-to-end data pipeline . This will include working with customers to define what “good” data looks like to implement the relevant workflows in their platform. Y ou will design innovative ML approaches t o enhance human-in-the-loop (HITL) techniques and improve the efficiency of data generation and review processes. Your team will own systems and tools that enable consistent, scalable, and high-quality data delivery to our customers. Sitting at the critical intersection of data engineering, ML engineering, operations, and customer engagement— leading scoping and preselling efforts. You'll also partner closely with the Snorkel delivery team and cross-functional stakeholders to define quality standards, develop measurement frameworks, drive ML-based workflows to improve data pipelines and unblock projects through technical innovation. As the founding member, you’ll also roll up your sleeves to define and own the workflows and processes that are needed to deliver exceptional data at scale. Main Responsibilities - Build and lead the Forward Deployed Engineering DaaS organization, setting a clear vision, defining the operating model and scaling its impact across Snorkel’s Expert Data-as-a-Service workflows - Build, mentor, and motivate high performing teams, including cultivating skills and culture needed to consistently deliver exceptional outcomes and transformative impact. - Own and evolve the data pipeline components of the DaaS stack, including model-assisted labeling and data generation, quality estimation, and data-centric feedback loops that guide human input - Partner with customers - including research and engineering teams at Frontier AI Labs - to scope requirements for complex, novel AI datasets and translate needs into delivery-ready workflows - Develop robust systems for request intake, task orchestration, SLA tracking, and progress monitoring to ensure seamless execution and prevent critical delivery gaps - Collaborate cross-functionally with research and engineering teams to innovate, develop, and productionize HITL data generation methods, advanced quality techniques, and improve internal delivery tooling - Drive continuous improvement by developing reusable workflows, surfacing operational insights, and enabling the organization to scale faster while maintaining high quality What We’re Looking For - 10+ years of experience in applied data or ML engineering roles, including 5+ years leading high-performing technical teams in hands-on management capacity - Demonstrated success in customer facing roles, with a strong enthusiasm for data pipelines and LLM-based workflows. - Proven track record of managing technical field teams in fast-paced, delivery-focused environments with competing priorities - Experience as a player-coach—comfortable being hands-on while supporting and scaling the team - Proven ability to thrive in fast-paced, ambiguous environments with cross-functional stakeholders - Strong practical experience with LLM-based workflows, Python, SQL, and data tooling (e.g., pandas, Plotly, Streamlit, Dash) - Bonus: experience working with data annotation workflows or internal tooling for data delivery orgs Why Join Snorkel AI? At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to found a function and have a broad, lasting impact across the company. The base salary range for this role is $268,800 to $403,200. This position may also eligible for benefits, variable compensation and equity, with final compensation determined based on factors such as experience, skills, qualifications, and location. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $268,800 - $403,000 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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Director, Forward Deployed Researcher - Data as a...

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! About the Role Snorkel AI is hiring a Director, Forward Deployed Researcher on the Data-as-a-service team. Your team will partner directly with frontier AI labs on their hardest data problems: scoping training data needs, designing RL environments, developing evaluation frameworks, and translating customer research objectives into actionable technical plans. This is a player-coach role. Roughly half your time goes to people leadership (hiring, coaching, developing your team) and the other half to hands-on technical work on strategic accounts. You set the technical bar for spec quality and build the playbooks and processes that let the team scale. You influence how the function operates, from process and standards to team culture. You partner closely with AE leadership on pipeline strategy, account coverage, and SE allocation. You also work cross-functionally with Delivery, Research, and Engineering to ensure smooth handoffs. The right person for this role can hold their own with frontier AI researchers, has strong presales instincts, and wants to build a team. Main Responsibilities - Recruit, hire, and develop the forward deployed research team, coaching the team on technical quality, customer engagement, and career growth - Set and maintain the technical bar for specifications, evaluation frameworks, and customer deliverables - Own SE capacity planning and account coverage; partner with AE leadership on deal strategy, engagement prioritization, and pipeline forecasting - Establish team processes and playbooks for discovery, spec development, and knowledge sharing - Lead technical conversations on select strategic accounts, including scoping, solution design, and partnership with frontier AI research teams - Serve as escalation point for technically complex or high-stakes customer situations Preferred Qualifications - 8+ years of experience in applied ML, AI solutions engineering, technical presales, or research-intensive customer-facing roles - 5+ years managing or leading technical individual contributors, ideally in a presales or solutions engineering org - Strong expertise in frontier AI concepts including LLMs, training data pipelines, evaluation methodologies, post-training techniques (RLHF, DPO, RLAIF), and domain areas such as coding agents, reasoning, multimodal models, or RL environments - Track record of hiring and developing technical talent - Experience operating against revenue or pipeline goals in a GTM organization - Excellent communication skills, able to coach a team, partner with sales leadership, and engage credibly with frontier AI researchers - Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration - B.S. in Computer Science, Machine Learning, or related field 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. The base salary range for this position is $228,800–$403,200, depending on location, experience, and qualifications. This role is also eligible for variable compensation and equity. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $228,000 - $403,200 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.

S

Software Engineer, Infrastructure

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 As a Software Engineer, Infrastructure at Sierra, you will be responsible for designing, building, and maintaining the core systems that make our AI platform possible. You’ll focus on making Sierra’s infrastructure secure, reliable, and scalable, enabling product teams to deliver with speed and confidence. - Ensure the reliability, scalability, and performance of our platform and LLM inference serving as we rapidly grow traffic. - Build and maintain cloud infrastructure using Terraform to ensure scalable, secure, and reproducible environments. - Create and maintain a self-serve infrastructure platform that enables the rest of engineering to deploy and operate services. - Own and evolve CI/CD pipelines and release management, enabling fast, reliable deployments for Sierra’s platform. - Architect and operate distributed systems that leverage distributed databases, retrieval systems, and ML models. - Develop and maintain core data serving abstractions along with authentication and security features (SSO, RBAC, authentication controls). - Navigate and integrate our stack with enterprise customer environments in scalable and maintainable ways. - Enhance observability tooling (metrics, logging, tracing) to provide deep visibility into platform health and performance. - Lead and participate in incident management, improving system resilience through proactive monitoring, root cause analysis, and postmortems. WHAT YOU’LL BRING - Strong software engineering background with 5–7+ years of hands-on development experience in highly technical products. - A strong inclination towards building automation, tooling, and platform, along with designing maintainable systems. - Proven experience with cloud platforms (AWS, GCP, or Azure) and infrastructure as code (Terraform preferred). - Hands-on expertise in CI/CD systems, release management, and container orchestration (e.g., Docker, Kubernetes). - Experience with observability tools (Prometheus, Grafana, Datadog, OpenTelemetry, etc.). - Experience in incident response and operating distributed systems in production. - Degree in Computer Science or related field, or equivalent professional experience EVEN BETTER… - Production experience working with LLMs and machine learning models. - Background in distributed systems, running SaaS services at scale, and agentic architecture. - Familiarity with security and authentication protocols (OAuth, SSO, mTLS). - Previous experience in a fast-paced startup environment or platform/infra-focused team. 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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Agent Experience Designer, Voice (Multilingual)

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 You’ll help shape and scale high-quality voice experiences across a global portfolio of AI agents. Working closely with cross-functional teams and customer stakeholders, you’ll combine systems thinking, design judgment, and operational rigor to improve voice quality, accelerate multilingual readiness, and create more human experiences. - Improve breadth and depth of voice quality: Evaluate, compare, and iterate on voices across multiple providers, languages, and use cases, ensuring experiences meet a high bar for clarity, naturalness, and appropriateness while continuously improving product and systems. - Support faster multilingual readiness and launches: Partner with product, engineering, and deployment teams to research solutions, evaluate options, identify risks, and guide voice-related decisions that enable timely, high-confidence launches across regions. - Design and apply effective and repeatable evaluations: Create and maintain structured methods for assessing voice performance and interaction quality, helping teams make informed tradeoffs and catch issues early. - Partner cross-functionally to resolve voice and interaction issues: Collaborate with engineering, product, and support teams to investigate quality gaps, diagnose root causes, and drive improvements through clear recommendations and follow-through. - Participate in customer-facing voice design work: Contribute to voice selection and design discussions with customers, helping them understand options, tradeoffs, and best practices while ensuring outcomes align with their goals. - Enable teams to scale voice design effectively: Design and develop guidance, examples, lightweight frameworks, and product enhancements, that empower product managers and engineers to make strong voice and interaction decisions independently. - Co-create agent personas and interaction patterns: Work with internal teams and customers to shape agent personalities, behaviors, and interactions, supporting distinct and intentional agent experiences as a trusted advisor. - Capture and share learnings: Document insights, patterns, and regional considerations to improve consistency, reduce repeated effort, and raise the overall quality bar across teams and deployments. WHAT YOU’LL BRING - 5+ years in a role that blends product craft with applied systems thinking (e.g., voice/AI experience design, conversation design, applied linguistics, UX for AI, product engineering with strong user experience focus, localization program leadership with quality ownership). - Strong ability to operate across many languages and priorities—you bring structure to ambiguity and can ship reliably while quality stays high. - Experience creating repeatable evaluation frameworks (rubrics, test suites, scorecards, QA gates) and using them to drive decisions. - Excellent communication: you can turn messy signals into clear guidance for engineering, product, and customer stakeholders. - Comfort living close to the work: deep knowledge of the product, understanding our customers and our customers’ customers, always listening, digging into edge cases, constantly improving the system as well as the product. EVEN BETTER… - Experience with speech synthesis and conversational agents. - Expertise with global language families. - Familiarity with provider and voice evaluation. - Linguistics, sound editing, and/or musical background. - Experience enabling other teams via playbooks, training, and internal tooling. 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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Principal Product Designer (R5250)

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. Job Description: The Special Projects team at Shield AI is an elite force within the office of the CTO. It consists of a group of very senior (L5-L8) and highly experienced software engineering experts from diverse fields (aerospace, robotics, cloud infrastructure, game development, interactive media design, ...). The charter of this group is to steer technology development towards strategic alignment with the CTO's vision, through tactical insertion into teams and technologies across the organization. Individuals within this team make direct and at times forward-sprinting contributions to all three pillars of Hivemind, Shield AI's software ecosystem for developing and deploying resilient intelligent teaming for aircrafts. Hivemind consists of four products: EdgeOS (C++ based high-performance middleware for autonomy development), Pilot (autonomy for the edge built atop EdgeOS; a models-based, modular and open architecture C++ codebase), Forge (Shield AI's AI Factory for the design, development, and testing of Hivemind Edge systems; a service-oriented architecture leveraged through an SDK, CLI, and web portal; a Go, Python, Typescript codebase), Commander (Software and hardware to support rich human-in-the-loop and human-on-the-loop interactions with the Hivemind; a C++-based back-end for interaction with Pilot; and a web-application UI for mission planning, command and control by operators implemented in a Typescript/React codebase). The Special Projects team is chartered to operate effectively within ambiguity. While this team does not own any software products, it tactically and strategically impacts the development of all foundational Hivemind products and leverages those technologies to rapidly deliver low-TRL solutions. This work happens shoulder-to-shoulder with the product teams in some cases, and in a forward-sprinting manner within the Special Projects team in other cases. The result is direct contribution to products in the former case, and development of reference implementations in the latter. The Special Projects team also functions as a pipeline for product and solution engineering teams across the organization. Individuals who enter the Special Projects team rapidly gain depth and breadth in their understanding of the Hivemind software ecosystem. This positions them well for leading technology development effort across the product and solution organization. What you'll do: Design and build the user experience for agentic AI products across the office of the CTO: an enterprise knowledge assistant (Brain), an agent-driven engineering platform (Springboard), and enterprise AI/data applications. Prototype in code. Ship working React/TypeScript implementations, not mockups — then land them in production with the product teams. Design the core interaction patterns of agentic software: streaming responses, citations and provenance, multi-modal input, artifact sharing, and human-in-the-loop review and approval surfaces. Design interfaces for foundation-model based autonomy architectures and model lifecycle tooling: evaluation dashboards, trace inspection, observability, and verification surfaces. Conduct user research across engineers, operators, and executives; define personae and translate findings into design decisions. Investigate competing agentic and enterprise AI products; synthesize findings for engineering and product leadership. Establish reusable design patterns and a component vocabulary for AI applications across the organization. Drive adoption: instrument what you ship, watch real usage, and iterate daily. Required qualifications: 12+ years designing and building production software interfaces, with end-to-end ownership from research through shipped product. Expert-level React/TypeScript; you write production-quality front-end code, not just prototypes. Portfolio of shipped tools or applications demonstrating strong interaction design and visual craft. Hands-on experience designing LLM-powered experiences: streaming, progressive rendering, loading states, context and conversation management. Deep knowledge of client and UI state management, the DOM, browser APIs, WebSockets, and Server-Sent Events. Experience running user research and usability testing, and acting on the results. Strong software hygiene: Git, code review, testing, CI/CD. Fluid intelligence that allows one to operate effectively in ambiguous conditions while finding opportunities to drive technical efforts and force multiply. Experience with Linux, Docker, and a major cloud platform (Azure, GCP, AWS). This role requires travel to a Shield AI location 1-3 times per quarter Preferred qualifications: Experience designing developer tools, IDEs, or engineering workflow products. Data visualization experience (D3, WebGL, dashboards); geospatial a plus. Experience building or maintaining a design system. Experience designing debugging/inspection UIs for generative AI systems. Experience with Electron (or similar). Experience with user experience design for video games or interactive media. Experience designing for "low code" or "no code" systems. Comfort reading C++ and/or Python and/or Go.

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Research Scientist

Negotiable

Scaled Cognition is the world’s only model lab dedicated exclusively to customer experience and pioneering agentic models purpose-built for reliable action-taking enterprise applications. Backed by Khosla Ventures, the company’s flagship Agentic Pretrained Transformer (APT) eliminates hallucinations, enforces enterprise policies and increases reliability in real-world CX workflows. Founded by serial AI entrepreneurs, former Microsoft Corporate Vice President of Conversational AI Dan Roth, and UC Berkeley AI Professor Dan Klein, and built by a team of world-class PhD researchers and engineers, Scaled Cognition advances the science of agentic AI to deliver safe, policy-aligned automation that enterprises can trust. As a Research Scientist at Scaled Cognition you will: - Develop and implement novel, next-generation neural architectures. - Work closely with machine learning and product engineers to direct research toward strategic product needs and align product development with novel model capabilities. - Stay up-to-date with the latest academic research and react quickly to new discoveries. You might be the right person for the job if you: - Enjoy exploring opportunities for innovation and aligning them with strategic objectives. - Can rapidly prototype and evaluate new modeling techniques. - Excel in collaborative, cross-functional team settings.

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Director, Product Management

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 Director, Product Management to define and execute the product vision for Runpod’s AI cloud platform. This role owns the end-to-end product lifecycle for our core offerings, including serverless endpoints, GPU instances (Pods), AI foundational services, developer APIs, and the overarching user experience. You will build and lead a high-performing product and design organization that intimately understands the needs of AI researchers, ML engineers, and developers. You will partner tightly with Infrastructure, Engineering, and GTM leadership. Your focus is ensuring our platform remains the fastest, most intuitive, and most cost-effective path to training and running massive AI inference workloads in the cloud. Responsibilities - Drive Product Vision & Strategy: Define the long-term vision and short-term roadmap for Runpod’s product suite. Anticipate the evolving needs of the AI/ML ecosystem and position Runpod as the premier infrastructure choice for AI builders. - Deep Customer Obsession: Act as the voice of the user. Spend significant time with ML engineers, AI startups, and enterprise customers to uncover friction points, gather requirements, and validate new feature concepts. - Build and Scale the Product Org: Hire, mentor, and grow a world-class team of product managers, UI/UX designers, and technical writers. Establish a culture of high standards, user empathy, and rapid iteration. - Cross-Functional Alignment: Partner seamlessly with the Director of Infrastructure and Director of Engineering to ensure roadmaps are technically feasible, highly reliable, and delivered on time. Translate complex infrastructure capabilities (like HPC networks and storage engines) into seamless developer experiences. - Pricing, Packaging, & GTM: Work closely with Marketing and Sales to define pricing models for compute, storage, and serverless usage that drive adoption while maintaining healthy margins. Guide product marketing on positioning and launch strategies. - Data-Driven Prioritization: Establish a rigorous, metrics-driven approach to product management. Leverage product analytics, churn data, and customer feedback to ruthless prioritize the backlog and maximize business impact. - Platform & Developer Experience (DX): Champion a world-class developer experience. Ensure our console, CLI, APIs, and SDKs are intuitive, well-documented, and reduce the time-to-value for new users to near zero. Requirements - Product Leadership Experience: 7+ years of product management experience, with at least 3+ years in a leadership role managing other PMs and designers in a hyper-growth environment. - Cloud or AI/ML Domain Expertise: Deep experience building SaaS, PaaS, IaaS, or developer-facing products. You must have a strong understanding of cloud infrastructure, GPUs, container orchestration (Kubernetes), and AI/ML workflows. - Technical Fluency: Ability to engage deeply with engineering and infrastructure leaders. You don't need to write code, but you must understand modern backend architectures, API design, and the complexities of distributed systems. - Proven Delivery Record: Demonstrated ability to take complex, ambiguous problems and turn them into successful, shipped products that drive measurable business outcomes. - Remote-First Operating Excellence: Experience building culture, alignment, and momentum across distributed technical and commercial teams. - Communication & Storytelling: Exceptional written and verbal communication. Ability to articulate a compelling product vision to the board, the company, and the broader developer community. - Background Check: Successful completion of a background check. Preferred Qualifications - Direct experience launching products specifically for ML Ops, model training, serverless inference, or LLM deployment. - A strong public presence or active engagement with the AI/ML and open-source developer communities. - Previous experience as a software engineer, ML engineer, or data scientist before transitioning to product management. What You’ll Receive: - The competitive base pay for this position ranges from ($225,000 - $325,000). 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.

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Director of Sales, Americas

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. You will lead Runpod's Americas sales team at a company growing quickly. We closed a $100M Series A in June 2026, serve more than one million developers, and run a hybrid go-to-market strategy that pairs product-led growth with direct sales. The team you lead is a mix of Account Executives and Account Managers, and it sits in a lean revenue organization alongside Forward Deployed Engineering, Marketing, Product, and Finance. Much of the operating model is still yours to build alongside a strong funnel to qualify and manage to maximize opportunity. As Director of Sales, you are accountable for the Americas revenue number and the health of the regional pipeline. Reporting to the COO/CRO, you will lead and coach the team, stay close to the biggest deals, and serve as the in-region escalation point. This is a role for someone who can guide a rep through a tough forecast, hold their own with a customer's executives, and put repeatable process behind both. You will shape how the Americas sells while the motion is still forming. Your job is to help a large developer base become contracted revenue and to make wins compound through expansion and renewal, and the model you set becomes what the rest of the region scales on. Responsibilities: - Own the number and pipeline: Own the Americas number and pipeline end to end across new business, expansion, and renewals, with a forecast the executive team can plan against. - Lead a combined AE and AM team: Run deal and account reviews, coach in the moment, and raise the standard of how the team qualifies, closes, and grows accounts. - Lead the biggest deals: Stay hands-on in the largest, most complex opportunities, including multi-year GPU capacity commitments, and handle executive engagement and master service agreement negotiations with Legal and Finance. - Convert the hybrid funnel: Work with Marketing and Product to turn product-led signups, target accounts, and inbound demand into qualified pipeline. - Protect and grow the base: Give expansion and renewals the same rigor as new business, and mature how the region keeps and expands accounts. - Build the region: Own the Americas go-to-market plan (segmentation, territories, target accounts), and hire, ramp, and put in place the playbooks and cadence the team runs on. - Be the voice of the market: Bring competitive and customer insight from the field back to Product and Marketing. - Make the team more efficient with AI: Look for ways to apply AI across the sales workflow, from prospecting to forecasting, so the team gets more done. Requirements: - 7+ years in quota-carrying B2B sales, including 3+ years as a frontline manager accountable for a team's number. - A verifiable track record of consistently meeting and exceeding sales targets. - Experience owning revenue beyond new business, including expansion and renewals. - The ability to sell to technical buyers such as ML engineers and data scientists as well as executive and economic buyers, with credible fluency in the AI and machine learning stack (for example, training versus inference). - Experience negotiating complex commercial agreements, including multi-year and master service agreements. - Strong CRM and sales enablement discipline and rigorous forecasting, plus a habit of using AI tools to work more efficiently. - A track record in an early-stage or high-growth environment where you built playbooks, and the self-direction to lead a remote team in a Slack-first, async culture. - Successful completion of a background check Preferred: - Experience selling infrastructure or a consumption or usage-based model. - Experience selling GPU cloud, AI infrastructure, MLOps, or cloud compute. - Experience leading a combined new-business and account-management (expansion and renewal) motion. - Experience running a sales motion on top of a self-serve or product-led funnel. WHAT YOU'LL RECEIVE: - The competitive OTE for this position ranges from 300,000 usd and 400,000 usd. This is a mix of base plus commission. The base 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 — we cover 100% for all employees and partial for dependents. - 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

Supply Lead

Negotiable

Claru (a Reka business line) · San Francisco or remote · Full-time Text has run out. The physical world — where skill lives — has never been recorded in a form a model can learn from. Claru is building that layer. We sell data to frontier labs and robotics companies: zero to seven figures of ARR in under six months, with a team of five. It is a two-sided marketplace: labs arrive with a research problem and a budget, not a specification; contributors and partner businesses capture the work they already do, paid per approved unit. This job is the supply side. We are hiring a Supply Lead and are looking for operators who are AI-native: more time with agents than with people, automation before hiring, systems that scale rather than teams that grow. That is how a team this small outruns much bigger companies. You need to be someone who can be dropped into an unfamiliar market and come back with capacity that is real. WHAT YOU'LL DO - Build a portfolio of partner businesses — whose staff capture the work they already do on shift — across the environments buyers want: event venues, retail back-of-house, construction, agriculture, hotels, warehousing. Each is won in a real conversation - Grow the contributor base alongside it and open new geographies, keeping supply owned and multi-channel so no single partner or geography is a dependency. - Own the geo rate card — what an approved unit costs by market and project type — so engagements are priced and staffed against something defensible - Own capacity forecasting — whether we can deliver a given volume in a given environment in a given number of weeks — which a Program Lead commits against. - Own contributor experience and retention: onboarding, payments landing on time, support, and why people stop. - Partner with the platform team on the tooling that makes supply self-serve A TYPICAL DAY There is no standard one, but a real day might run like this: you start on the partner businesses already live — chasing the ones gone quiet, calling the two that signed last month and have not filmed. Contributor payments in one market did not land, so you find where it stopped and clear it. A Program Lead needs to know whether we can cover four weeks of warehouse capture in a new country, and you give them a number you would stand behind. Then a first conversation with a food manufacturer about putting their team on shift and collecting data. WHAT WE'RE LOOKING FOR - Early-stage marketplace operating experience — required. You have got one side of a flywheel turning from a standing start, in a new territory or a new vertical — recruitment, pricing, retention and economics. Every new market here is that problem again - AI-native — required. You spend more time with agents than with people, you automate a process before you staff it, and your answer to more volume is a better system rather than more headcount - You have operated, not only advised. Former founders, high growth early stage startup hires, consulting, or business operations background is a strong plus and exactly the rigour we want - You close business yourself: partner businesses are won in conversation - Numerate — you build the model, find where the cost per unit breaks, and argue for the change - Credible with a company owner and with a shift supervisor alike - Bonus: exposure to AI labs or training data pipelines REKA'S MISSION Claru is a part of Reka (VC backed by NVIDIA). Reka's mission is to build useful multimodal artificial intelligence and use it to empower organisations and businesses. We are a globally distributed foundation model startup, headquartered in the San Francisco Bay Area, California. Embracing a remote-first approach, our team brings together top talent from around the world. Our founding team, along with many of our team members, has contributed to many of the breakthroughs in AI over the past decade. Reka was founded by researchers from Google DeepMind and FAIR, and ships Reka Vision, launched in July 2026, and Reka Edge, a 7B vision-language model. Reka's leadership includes the team behind Moonvalley's Marey, the first video model trained entirely on licensed data. Claru is Reka's data business, supplying the training data behind physical AI, embodied AI and world models. WHY REKA? - An Elite Team: Collaborate with top-tier engineers, researchers, operators from renowned organizations like Google DeepMind and Facebook AI Research (FAIR) and successful startups, driving innovation in cutting-edge AI technology. - Massive Market Opportunity: Be part of a rapidly growing industry poised to transform multiple sectors globally, offering the chance to make a significant impact. - Mission-Driven Environment: Work alongside a collaborative, mission-focused team dedicated to advancing AI for meaningful applications. - Inclusive and Open Culture: Thrive in an open and inclusive work environment that values diverse perspectives and fosters creativity. - Generous Benefits: Enjoy 5 weeks of paid leave to recharge, comprehensive healthcare benefits including vision and dental, and additional perks that support your well-being.

R

Program Lead

Negotiable

Claru (a Reka business line) · San Francisco · Full-time Text has run out. The physical world — where skill lives — has never been recorded in a form a model can learn from. Claru https://claru.ai is building that layer. We sell data to frontier labs and robotics companies: zero to seven figures of ARR in under six months, with a team of five. It is a two-sided marketplace: labs arrive with a research problem and a budget, not a specification; contributors and partner businesses capture the work they already do, paid per approved unit. Both sides get built engagement by engagement. That is this job. We are hiring a Program Lead and are looking for operators who are AI-native: more time with agents than with people, automation before hiring, systems that scale rather than teams that grow. That is how a team this small outruns much bigger companies. You need to be someone who can be dropped into an unfamiliar problem, with a client waiting, and come back with a plan that holds. WHAT YOU'LL DO - Turn an open-ended research need into a program with a scope, a standard, a timeline and agreed economics — then own the number - Work directly with frontier labs and robotics teams: turn a research problem into something deliverable, keep sophisticated buyers confident, and own the relationship from first call to delivery - Partner with our supply team to build the supply side from nothing if needed: recruit, activate and retain the contributors and partner businesses a program needs, in industries and geographies where none exists - Go at whichever blocker is in front of you: some days a spec or a tooling gap, other days a person, a partner or an incentive - Act as product manager for the platform team: your field evidence decides what gets built next, and you speak for the contributor and the client in the roadmap - Go where the work happens; physical operations are won by whoever showed up - Build leverage as you go — agents, automation, playbooks — so the second program costs a fraction of the first and the tenth runs itself A TYPICAL DAY There is no standard one, but a real day might run like this: you start on the programs already live — checking with the team that supply is filming, that yesterday's batches cleared the quality bar, and chasing the sites that have gone quiet. A buyer wants to change the spec mid-program, so you work out what that does to volume, timeline and cost, and get the new version agreed in writing. Then a first call with a lab you have not worked with before: understand what they are trying to train, and be straight about what we can produce and what we cannot. WHAT WE'RE LOOKING FOR - Early-stage marketplace operating experience — required. You have had to get a flywheel turning from a standing start, in a new territory or a new vertical, where neither side existed yet and you had to build both. Every engagement here is that problem again - AI-native — required. You spend more time with agents than with people, you automate a process before you staff it, and your answer to more volume is a better system rather than more headcount - You have operated, not only advised. Former founders, high growth early stage startup hires, consulting, or business operations background is a strong plus and exactly the rigour we want - Structured problem solving under pressure: first principles, then a plan a client and a team can both act on - Numerate — you build the model, find where the economics break, and argue for the change - Credible with a research lead and with a shift supervisor alike - Bonus: exposure to AI labs or training data pipelines REKA'S MISSION Claru is a part of Reka (VC backed by NVIDIA). Reka's mission is to build useful multimodal artificial intelligence and use it to empower organisations and businesses. We are a globally distributed foundation model startup, headquartered in the San Francisco Bay Area, California. Embracing a remote-first approach, our team brings together top talent from around the world. Our founding team, along with many of our team members, has contributed to many of the breakthroughs in AI over the past decade. Reka was founded by researchers from Google DeepMind and FAIR, and ships Reka Vision, launched in July 2026, and Reka Edge, a 7B vision-language model. Reka's leadership includes the team behind Moonvalley's Marey, the first video model trained entirely on licensed data. Claru is Reka's data business, supplying the training data behind physical AI, embodied AI and world models. WHY REKA? - An Elite Team: Collaborate with top-tier engineers, researchers, operators from renowned organizations like Google DeepMind and Facebook AI Research (FAIR) and successful startups, driving innovation in cutting-edge AI technology. - Massive Market Opportunity: Be part of a rapidly growing industry poised to transform multiple sectors globally, offering the chance to make a significant impact. - Mission-Driven Environment: Work alongside a collaborative, mission-focused team dedicated to advancing AI for meaningful applications. - Inclusive and Open Culture: Thrive in an open and inclusive work environment that values diverse perspectives and fosters creativity. - Generous Benefits: Enjoy 5 weeks of paid leave to recharge, comprehensive healthcare benefits including vision and dental, and additional perks that support your well-being.

R

Research Program Manager - Model Development

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 engineering 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 role is embedded directly within our Pre-training ML and Data teams, working alongside our Pre-training research leads at the core of how we build frontier models. You will be deeply involved in the research lifecycle, from data pipeline coordination and experiment planning to model architecture decisions and scaling strategies. Your job is to understand how our researchers work, identify the highest-leverage gaps, and build the programs, processes, and coordination structures that let them focus on pushing the frontier rather than navigating organizational complexity. 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 - Embed within the pre-training ML and Data teams to deeply understand the technical landscape, build trust with researchers and technical leads, and identify where programs and process can have the most impact on research velocity. - Drive end-to-end execution of complex, cross-team research initiatives that span data, model architecture, training runs, and evaluation, often without established playbooks. - Coordinate the operational rhythm of pre-training research, including experiment prioritization, run scheduling, data readiness, and checkpoint handoffs to downstream teams. - Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations rather than status updates. - Build lightweight processes that bring structure to unstructured research environments without adding friction. Create coordination mechanisms for cross-team handoffs, config management, and research milestones that replace ad hoc Slack threads with durable, visible systems. - Act as the connective tissue between pre-training, mid-training, post-training, and infrastructure teams, ensuring that upstream decisions propagate cleanly and downstream teams are never surprised. ABOUT YOU - 7+ years of experience in technical program management, research operations, or ML engineering, with a track record of building programs from scratch in research or ML-heavy environments. - Deep enough technically to engage with researchers on topics like model architecture, scaling laws, data processing pipelines, training dynamics, and optimizer behavior. You don't need to run the experiments yourself, but you need to follow the reasoning and spot risks early in order to discuss tradeoffs. - Proven ability to embed within technical teams and earn trust through competence, reliability, and genuine curiosity about the work. Researchers want to work with you because you make them faster, not because the process says they have to. - Resourceful and high-agency. You navigate ambiguity and shifting priorities without losing momentum. You figure out what needs to happen and you make it happen. - Strong stakeholder management skills with the ability to influence senior technical staff through consistent delivery and well-informed judgment, not authority. - Comfortable in high-stakes environments where decisions impact model quality, compute spend, and training timelines measured in weeks or months. - Excited to build from zero to one. We are a small, fast-moving team and this role will help define how research program management works at Reflection. - Motivated by enabling researchers and engineers to build the world's most capable open-weight AI systems. 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

R

Member of Technical Staff

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. 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 As a Research Software Engineer on the Safety team, you will design, build, and own the infrastructure used to run our most sensitive model evaluations — including evaluations in CBRN (chemical, biological, radiological, and nuclear), child safety, and other dangerous-capability domains. These evaluations inform release decisions for our open models, so the systems you build must be secure, isolated, reproducible, and trustworthy under scrutiny. This is a deeply technical, high-ownership role at the intersection of platform engineering, security, and safety research. You will partner closely with domain experts, legal, and safety researchers to turn their evaluation needs into robust, scalable infrastructure: sandboxed execution environments, controlled data pipelines for sensitive material, access controls, audit logging, and the tooling that lets researchers safely elicit and measure model capabilities in high-consequence areas. WHAT YOU'LL DO - Design and build secure, sandboxed infrastructure for running sensitive model evaluations, including CBRN and other dangerous-capability domains. - Build controlled data pipelines and storage for sensitive evaluation material, applying least-privilege and need-to-know access, role-based access control (RBAC), encryption at rest and in transit, audit logging, and data-minimization safeguards. - Partner with safety researchers and domain experts to translate evaluation designs into reliable, reproducible, and scalable systems. - Build eval-orchestration tooling and harnesses that let researchers run high-throughput evaluations against models and agents in isolated environments. - Develop infrastructure for measuring AI capability uplift in high-consequence domains, and integrate results into the pipelines that inform release decisions. - Implement guardrails, monitoring, and compartmentalization so sensitive work stays appropriately siloed, applying least-privilege, need-to-know, and defense-in-depth principles across compute, data, and tooling. - Write production-quality Python (and related tooling) for high-throughput data processing and evaluation systems. - Improve the reliability, security posture, and developer experience of the safety team's evaluation platform over time. ABOUT YOU - Strong software engineering skills, particularly in Python, with a track record of building reliable, scalable infrastructure or platform systems. - Experience building sandboxed, isolated, or otherwise security-sensitive execution environments (e.g., containerization, VM isolation, secure compute) for Trust and Safety teams. - Solid grounding in security engineering fundamentals: principle of least privilege, need-to-know access, role-based access control (RBAC), secrets management, encryption, audit logging, compartmentalization, and defense-in-depth design. - Experience building data pipelines and handling sensitive or restricted data with appropriate safeguards. - Ability to own entire problems end-to-end, including ambiguous, cross-functional ones. - Comfort working on sensitive projects that require discretion, integrity, and sound judgment. - Thrive in a fast-paced, high-agency startup environment with a bias toward action. STRONG CANDIDATES MAY ALSO HAVE - Experience building evaluation, benchmarking, or experimentation infrastructure for ML systems. - Experience working with LLMs, agents, or ML training/inference pipelines. - Familiarity with dangerous-capability or dual-use domains (CBRN, cyber, etc.) and the information-security considerations they involve. - Familiarity with compliance frameworks relevant to sensitive data handling. We encourage you to apply even if you don't meet every qualification. Not all strong candidates will match every item listed. 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

Q

Senior Data Engineer

Negotiable

On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise. Our success in rapid scale across the globe is backed by some of the world's leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations. #LI-MB1 Qventus is looking for a Senior Data Engineer to pioneer new data solutions at Qventus. Our Data team ensures that Qventus data users have the data they need to build and drive new and existing Qventus products at scale and cost. This includes bidirectional integration with hospital EMR sources via multiple channels (eg. FHIR), complex highly secure (HIPAA) transformations capable of normalizing information across various workflows and client nuances, integration with & discovery of multiple third party datasets from customer data to big-data claims, and more. Our products span the machine learning based orchestrations, real time hospital reporting, analytical insights, and interactive applications needed to improve the lives of patients and doctors across the country. As a Senior Data Engineer, you will lead the design, development, and management across investments data assets & data pipelines. You will identify, monitor, and lead initiatives to collect, explore, and model data in close partnership with our data modeling lead and integrations partners. You will work closely with solution experts to design, iterate, and develop strategic enhancements to our Qventus foundational data model. You will be adept in partnering with cross-functional partners and data users to translate functional needs into data requirements and quality checks. You will be data curious and excited to have an impact on the team and in the company and to improve the quality of healthcare operations. Key Responsibilities - Spearhead the discovery, evaluation, and integration of new datasets, collaborating (incl. pipeline development and data modeling/documentation) working closely with key data stakeholders to understand their impact and relevance to our core products and the healthcare domain. - Facilitate the technical management of data assets - clearly tracking and maintaining context on the data within the dataset lifecycle and sustaining tight partnerships with immediate partners on ingestion & solution data engineering - Translate product / analytical vision into highly functional data pipelines supporting high quality & highly trusted data products (incl. designing data structures, building and scheduling data transformation pipelines, improving transparency etc.). - Set the standard for data engineering practices within the company, guiding the architectural approaches, data pipeline designs, and the integration of cutting-edge technologies to foster a culture of innovation and continuous improvement. What We’re Looking For - Excellent cross-functional communication - the ability to break down complex technical components for technical and non-technical partners alike - Innate aptitude for interpreting complex datasets with demonstrated ability to discern underlying patterns, identify anomalies, and extract meaningful insights, demonstrating advanced data intuition and analytical skills. (Healthcare experience preferred) - Excellence in quality data pipeline design, development, and optimization to create reliable, modular, secure data foundations for the organization's data delivery system from applications to analytics & ML - Proven ability to independently handle ambiguous project requirements and lead data initiatives from start to finish, while collaborating extensively with cross-functional, non-technical teams to inform and shape product development. Bonus Points For - 5+ years of experience designing, building, and operating cloud-based, highly available, observable, and scalable data platforms utilizing large, diverse data sets in production to meet ambiguous business needs - Relevant industry certifications in a variety of Data Architecture services (Databricks Certified Data Engineer Professional, AWS Certified Data Engineer or Solutions Architect Professional, SnowPro® Advanced Architect/Data Engineer, Microsoft Fabric Data Engineer, and dbt Analytics Engineering). - Experience with MLOps and/or developing and maintaining machine learning models and infrastructure - Experience with data visualization tools and analytics technologies (Sigma, Looker, Tableau, etc.) - Degree in Computer Science, Engineering, or related field Compensation for this role is based on market data and takes into account a variety of factors, including location, skills, qualifications, and prior relevant experience. Salary is just one part of the total rewards package at Qventus. We also offer a range of benefits and perks, including Open Paid Time Off, paid parental leave, professional development, wellness and technology stipends, a generous employee referral bonus, and employee stock option awards. Salary Range $150,000 - $180,000 USD Qventus values diversity in its workforce and proudly upholds the principles of Equal Opportunity Employment . We welcome all qualified applicants and ensure fair consideration for employment without discrimination based on any legally protected characteristics, including, but not limited to: veteran status, uniformed service member status, race, color, religion, sex, sexual orientation, gender identity, age, pregnancy (including childbirth, lactation and related medical conditions), national origin or ancestry, citizenship or immigration status, physical or mental disability, genetic information (including testing and characteristics) or any other category protected by federal, state or local law (collectively, "protected characteristics"). Our commitment to equal opportunity employment applies to all persons involved in our operations and prohibits unlawful discrimination by any employee, including supervisors and co-workers. Qventus participates in the E-Verify program as required by law and is committed to providing reasonable accommodations to individuals with disabilities in compliance with Americans with Disabilities Act (ADA) . In compliance with the California Consumer Privacy Act (CCPA) , Qventus provides transparency into how applicant data is processed during the application process. Candidate information will be treated in accordance with our candidate privacy notice. *Benefits and perks are subject to plan documents and may change at the company's discretion. *Employment is contingent upon the satisfactory completion of our pre-employment background investigation and drug test.

P

Applied AI: Product Strategy & Revenue Lead

Negotiable

APPLIED AI PRODUCT STRATEGY & REVENUE 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 This is not a traditional sales role. It is not a traditional product role. It is not a traditional solutions engineering role. You will help define how Prime Intellect turns frontier post-training infrastructure into a product customers can understand, buy, deploy, and expand. Today, the hardest part of the business is not selling raw compute. It is refining the product, customer motion, and technical wedge together with Applied Research, Product, Engineering, and the customer. We are selling something much more complex and much more valuable than GPUs: the ability for customers to build their own lab — environments, evals, verifiers, agents, training runs, and deployment loops that compound over time. You will own that messy middle. You will work directly with customers, the CEO, GTM leadership, Applied Research, and Engineering to translate ambiguous customer pain into a concrete product strategy, technical scope, commercial proposal, and path to revenue. You will help us figure out where the product is ready, where it needs to be shaped, what the customer actually wants, and how to turn early traction into repeatable motion. This is a role for someone who wants to be in the room where a new category is being created. WHAT YOU’LL OWN CUSTOMER-TO-PRODUCT TRANSLATION You will work with frontier AI labs, fast-growing AI startups, and enterprise AI teams to understand what they are trying to build, where their current stack breaks, and how Prime Intellect can become the infrastructure layer underneath their post-training and agent workflows. You will turn vague, high-stakes customer conversations into clear technical and commercial strategy: - What is the customer actually trying to improve? - Is the wedge compute, evals, environments, sandboxes, managed RL, SFT, inference, or a full-stack workflow? - What should Applied Research build or prototype? - What needs to be packaged as product? - What should be in scope for a POC versus a long-term deployment? - What is the fastest path to a strong yes? PRE-PMF PRODUCT STRATEGY You will help shape Prime Intellect’s product motion before every part of the playbook is obvious. That means identifying patterns across customer conversations, building repeatable narratives, defining packaging, sharpening use cases, and helping the team understand which customer asks are one-off noise versus signs of a massive market. You will help answer questions like: - How do we explain Lab to different customer segments? - Which customer workflows should become reference architectures? - What should we productize versus deliver as managed work? - Where is the strongest wedge for enterprise customers? - Which signals show that a customer is ready for managed post-training? - How do we turn Applied Research work into revenue without diluting the research agenda? REVENUE OWNERSHIP You will own high-value customer opportunities from first serious conversation through qualification, scoping, proposal, POC, procurement, and expansion. You will not be measured on activity. You will be measured on whether the most important customers move. This includes: - Running discovery with technical and executive stakeholders - Building the business case and technical wedge - Owning account strategy with leadership - Drafting proposals, scopes, and commercial structures - Coordinating internal workstreams across Applied Research, Product, Engineering, Legal, and Finance - Creating momentum through ambiguity - Turning early deployments into expansion and long-term platform revenue APPLIED RESEARCH PARTNERSHIP You will work extremely closely with Applied Research. The best version of this role has enough technical taste to understand where an RL/post-training workflow is real, where a customer is hand-waving, and where a sharp Applied Research prototype could unlock a major deal. You will help Applied Research prioritize customer-facing work by bringing signal from the field: - Which evals matter? - Which environments should we build? - Which agents or workflows are most commercially valuable? - Which technical demos will change the customer’s mind? - Which customer problems are actually research problems in disguise? CATEGORY CREATION The market understands compute. It does not yet fully understand full-stack post-training infrastructure. You will help write the playbook. You will contribute to positioning, sales narratives, customer decks, case studies, reference architectures, launch moments, and internal strategy. You should be able to turn raw customer conversations into crisp language the entire company can use. WHAT WE’RE LOOKING FOR We are looking for exceptional generalists with rare taste across AI, product, customers, and commercial strategy. You might come from: - Technical GTM at a frontier AI, infra, devtools, or enterprise software company - Founder or early operator experience at an AI startup - Product or strategy at a highly technical company - Forward-deployed engineering, solutions, or applied AI work - Investing, venture, or strategic finance with deep AI infrastructure exposure - Research-adjacent roles where you worked directly with customers or product teams You should have: - Strong product and commercial judgment - Ability to understand technical products quickly - Excellent written communication - High agency and comfort with ambiguity - Taste for what makes a customer problem real - Ability to work with researchers, engineers, executives, and operators - Sharp instincts around enterprise buying, POCs, procurement, and expansion - Obsession with AI, post-training, agents, evals, and infrastructure - Ability to create structure where none exists You do not need to be a researcher, but you should be technical enough to earn trust with researchers and customers. You do not need to be a traditional salesperson, but you should be commercially intense enough to close. You do not need to be a PM, but you should have strong product taste. BONUS POINTS - Experience with RL, SFT, evals, agent frameworks, or LLM post-training - Experience selling or deploying infrastructure, AI platforms, devtools, or enterprise AI products - Experience working with frontier AI labs, model companies, or infra-heavy startups - Ability to write excellent customer-facing decks, memos, proposals, and launch narratives - Strong network across AI startups, research labs, or enterprise AI teams - Founder mentality and willingness to do unglamorous work to win WHY THIS ROLE Most GTM roles ask you to sell a product someone else already defined. This role asks you to help define the product, the market, the motion, and the revenue engine at the same time. You will work on the hardest commercial and product questions at one of the fastest-growing companies in AI infrastructure. You will sit close to customers building real AI systems, researchers pushing the frontier of post-training, and leadership making company-defining decisions. If you want a clean playbook, this is not the role. If you want to help invent the playbook for how frontier AI infrastructure gets built, packaged, sold, deployed, and scaled, this is the role. 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 - A front-row seat to building the infrastructure layer for open AI READY TO BUILD THE COMMERCIAL ENGINE FOR OPEN SUPERINTELLIGENCE? Apply to help Prime Intellect turn frontier post-training infrastructure into the product, platform, and customer motion that powers the next generation of AI systems.

P

Member of Engineering (Inference Infrastructure)

Negotiable

ABOUT POOLSIDE In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers. Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises. ABOUT OUR TEAM We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year. Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development. ABOUT THE ROLE You’ll be working in the compute team focusing on GPU workload scheduling and inference serving optimization. You would partner with the inference team to improve our inference throughput and latency for evals and reinforcement learning. You would collaborate with our scalability team to focus on stabilizing our large scale fault tolerant training. You would also be in close contact with the infra team to make sure our GPU nodes are all healthy and fully utilized. We are one of the key teams to improve the research velocity. Any improvement on our systems has a wide impact on researchers and can contribute to the poolside mission on building a frontier model. YOUR MISSION To optimize GPU utilization across the company and to deliver stable and scalable inference serving stack for Poolside’s researchers. RESPONSIBILITIES - Design and develop internal scheduling system to maximize GPU utilization - Build API and tooling to help manage the lifecycle of GPU workloads and troubleshoot failures - Design and improve inference control plane to speed up model deployment and inference request serving - Collaborate with research to improve research velocity continuously SKILLS & EXPERIENCE - Strong programming skills in Go, or other similar languages - Strong systems engineering background: distributed systems, schedulers, control planes, or high-throughput data planes. - Production experience with Kubernetes internals — controllers, informers, operators — not just deploying to it. - Bias toward observability and debuggability: building a system that is easy to navigate when debugging production issues - Plus: experience in systems serving large scale inference requests PROCESS - Intro call with a member of our team - Technical Interview(s) with one of our Members of Engineering - Team fit call with the People team - Final interview with one of our Founding Engineers BENEFITS - Fully remote work & flexible hours - 37 days/year of vacation & holidays - Health insurance allowance for you & dependents - 16 weeks of flexible, full-pay parental leave - Well-being, always-be-learning & home office allowances - Company-provided equipment - Frequent team get togethers - Diverse & inclusive people-first culture - Diverse & inclusive people-first culture

P

Staff Backend Software Engineer - GO & Python

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. The Role PhysicsX is building a platform that enables Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The platform handles massive volumes of complex simulation data and enables high fidelity multi physics simulation through AI inference. We are looking for a Senior (potentially Staff level, depending on the candidate) Software Engineer to join the AI Workbench platform team. This is a software engineering role, not an ML research role. You do not need to be a practising ML engineer, but you do need to be fluent in the language of ML and data platforms, ideally through prior hands on experience with ML platforms or data science work. The team sits in a highly infrastructure heavy domain without being an infrastructure team itself. A large part of the role is building strong working relationships with the infrastructure team and owning the work that falls in the space between the two, where ownership is not always clearly defined. You will also play a key role in mentoring and developing engineers across the team, some of whom are early career and some mid career. Note for candidates: PhysicsX is currently going through a team reorganisation. The scope of this role may evolve as team structures settle, and we have kept this description intentionally broad rather than narrowly tailored to today's team shape. What You Will Do - Design and build distributed systems, services, and APIs for high dimensional simulation data across the machine learning lifecycle, from data processing and model training to inference services. - Own work end to end in a domain where responsibilities often sit between the AIWB platform team and the wider infrastructure team, and proactively build the relationships needed to manage that ambiguity well. - Build tools that enable data scientists and engineers to create automated, robust pipelines for data ingestion and processing, powering active learning loops. - Architect and integrate storage solutions, data warehouses, and data lakes to handle the demands of complex simulations, multimodal data, and deep learning workloads. - Deploy and support services across more than one production environment, including cloud and on premises or air gapped customer environments. - Define system architecture for new capabilities, making trade offs across performance, reliability, cost, and developer experience. - Author and review Technical Decision Records and contribute to Technology Radar reviews. - Mentor and support the growth of engineers across the team, including those earlier in their careers, and act as a technical role model. - Remain genuinely hands on. Writing code stays part of the job at every level, this is not a purely managerial track. - Communicate clearly and confidently with a wide range of stakeholders and personalities across engineering, delivery, and customer facing teams. What You Bring to the Table - A passion for the craft. You are driven by engineering excellence and enjoy building things properly, not just shipping quickly. - Strong Kubernetes experience. This is a must have, including comfort with CRDs, Operators, and infrastructure configuration tools such as Helm or ArgoCD. - Strong command of Go, plus working knowledge of Python. Comfort with Rust is also valued. Experience across Kubernetes, Go, Python, and Rust is ideal. - Experience deploying and supporting services in more than one production environment, not just a single deployment target. - An ML platform background or prior data science experience, even if you have since moved into pure software engineering. You understand how ML practitioners and researchers think and talk about problems. - Intellectual curiosity and a proactive approach. You do not need to be told what to do next. - Strong communication and organisational skills, with experience operating at a senior or staff level, or equivalent people leadership experience, in a comparable engineering organisation. - Comfort with ambiguity and an adaptable, startup minded approach. PhysicsX moves quickly and priorities shift, and we are looking for people who thrive in that environment rather than finding it difficult. - Prior experience mentoring or developing other engineers. Ideally - Experience at an ML focused startup or platform company, or a strong infrastructure background from a larger organisation such as a bank, provided you can demonstrate adaptability to a smaller, faster moving environment - Side projects, open source contributions, or things you have built for their own sake. Not a requirement, but a nice signal of genuine engineering passion. - Understanding of what it takes to build and support software across cloud, on premises, and air gapped environments. - Experience with big data systems and analytics at scale in production. What We Offer - Equity options, share in our success and growth. - 10% employer pension contribution, invest in your future. - Free office lunches, great food to fuel your workdays. - Flexible working, balance your work and life in a way that works for you. - Hybrid setup, enjoy our new Shoreditch office while keeping remote flexibility. - Enhanced parental leave, support for life's biggest milestones. - Private healthcare, comprehensive coverage. - Personal development, access learning and training to help you grow. - Work from anywhere, extend your remote setup to enjoy the sun or reconnect with loved ones. 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

Principal Data Scientist / Algorithm 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. Who We're Looking For As a Principal Data Scientist (Algorithm Engineer) in Delivery, you are an experienced problem solver and technical leader who is passionate about building practical solutions that enable customers to make better engineering decisions. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embeds cutting edge AI models into tools that are useful and used. You've consistently tackled difficult problems that require strong foundations in data driven modelling and deep learning techniques, with extensive hands-on experience in probabilistic methods and predictive modelling. Your expertise in python, along with advanced proficiency in libraries like NumPy, SciPy, Pandas, TensorFlow and PyTorch, is essential, with proven ability to architect and deploy scalable, production-ready models and data pipelines. With industry experience (post Masters of PhD) in a commercial, you're ready to not only execute but also lead and mentor others. You're truly excited about growing both your technical expertise and leadership skills, naturally taking ownership of complex data science work streams and guiding teams to success. You continuously improve the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. 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, while providing technical leadership to your team. You'll build the foundations for successful, impactful solutions by: - Leading pre-processing and analysis of complex data to prepare it for use in predictive modelling, establishing best practices and methodologies for your team. - Architecting and developing innovative deep learning models in combination with state-of-the-art optimisation methods to predict and control the behaviour of physical systems. - Taking full responsibility for the quality, accuracy and impact of your work and the work of your team. - Designing, building, and testing data pipelines that are reliable, scalable, and robustlyeasily deployable in production environments. - Leading cross-functional collaboration with simulation engineers to ensure seamless integration of data science models with simulations. - Driving internal R&D and product development, helping to refine models and identify new areas of application. - Mentoring junior team members and providing technical guidance to help them grow. - Leading open communication and presentations with both technical teams and customers, helping onboard users and co-develop with customers. - Representing PhysicsX as a technical authority when traveling to customer sites in North America, Europe, Asia, Oceania, an average of 2-3 weeks per quarter, where you'll collaborate closely with customers to build solutions on site. As a senior member of the team, you'll have significant influence on our technical direction and the opportunity to shape future solutions and products, while developing your skills as a technical leader. 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! 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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Research Scientist, Scaling RL

Negotiable

ABOUT PERIODIC LABS 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 tasks. You’ll study how RL scales with training compute, develop better algorithms, and take ideas from controlled experiments to our largest runs like Periodic Neon https://periodic.com/news/nature-is-our-learning-environment. WHAT YOU'LL DO - Design experiments to understand how RL performance scales with compute, model size, data, and reward quality, building on work such as ScaleRL https://arxiv.org/abs/2510.13786 - Develop better RL algorithms, spanning policy optimization, advantage estimation, exploration, and credit assignment for long-horizon RL tasks - Build adaptive sampling and curriculum methods that adjust task difficulty, problem selection, and the number of rollouts as models improve - Study bias and stability during RL training, including importance-sampling corrections and methods to tackle policy staleness and training–inference mismatch, as discussed here https://www.youtube.com/watch?v=GH4JCdAAUYg. - Improve compute efficiency across training and inference through experiments with hyperparameters, such as length penalties, rollout counts, batch sizes, and update schedules. YOU WILL THRIVE IN THIS ROLE IF YOU HAVE - Hands-on experience training LLMs with reinforcement learning - Strong attention to detail and rigorous approach to answer questions scientifically. - Coming up with small-scale RL setups that transfers to large-scale training runs. - Comfort working across a complex training stack to implement, debug, and test new research ideas. Mechanics Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA or Montreal, Canada Compensation: $225,000-$350,000 base + equity Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.

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Research Scientist, Condensed Matter Theory

Negotiable

ABOUT PERIODIC LABS 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 an insatiable drive to push the boundaries of what’s scientifically possible. ABOUT THE ROLE Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale. As a Research Scientist in Condensed Matter Theory, you will use theoretical modeling to connect first-principles calculations and experiments. You will collaborate closely with computational and experimental scientists and ML researchers to develop physical understanding that guides and accelerates the discovery of novel quantum materials. WHAT YOU’LL DO - Develop and apply theoretical models to interpret experimental observations and guide materials discovery efforts - Bridge first-principles calculations (e.g., DFT) and experimental results to build predictive physical understanding - Collaborate with ML researchers to incorporate theoretical insights into machine learning models and inform training data strategies - Work with computational and experimental scientists to design experiments and validate theoretical predictions - Communicate theoretical findings clearly across disciplines and contribute to a shared scientific roadmap YOU WILL THRIVE IN THIS ROLE IF YOU HAVE - PhD in condensed matter theory, with a focus on quantum materials - Deep expertise in relating theoretical models to real materials and experimental observables - Strong publication record demonstrating impactful, independent research - Ability to collaborate effectively across theory, computation, and experiment ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE - Experience running first-principles calculations such as density functional theory (DFT) on realistic systems - Experience with deep learning methods, including graph neural networks, applied to materials or physics problems - Experience modeling superconductivity and/or magnetism in quantum materials - Familiarity with high-throughput computational workflows or materials databases MECHANICS Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA or Montreal, Canada Compensation: $225,000–$325,000 + equity Visa sponsorship: Yes, we sponsor visas.

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Computational Scientist, Structural & Thermal

Negotiable

ABOUT PERIODIC LABS Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, semiconductors, 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 an insatiable drive to push the boundaries of what’s scientifically possible. ABOUT THE ROLE Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to develop structural, thermal, and coupled thermo-mechanical simulation capabilities for semiconductor systems and advanced materials. This role is for someone who thinks of themselves as both a scientist and a software engineer. You understand solid mechanics and heat transfer at a level that goes beyond configuring a commercial FEA package, and you are comfortable building, extending, or automating solvers when the physics or scale of the problem requires it. WHAT YOU'LL DO - Develop agent-based structural and thermal simulation capabilities for semiconductor systems, including wafer stress and warpage, thin-film residual stress, thermo-mechanical reliability, thermal budget modeling, process-induced deformation, fracture and delamination, and coupled heat-stress problems. - Build or extend custom solvers where commercial FEA tools are too slow, too opaque, or insufficiently flexible. This may include custom FEM implementations, phase-field fracture models, crystal plasticity codes, thin-film mechanics frameworks, or reduced-order mechanical models, written in Python, C++, or Julia. - Model materials behavior at the level the physics requires: elasticity, plasticity, viscoelasticity, creep, fracture, diffusion-induced stress, thermal expansion mismatch, interfacial mechanics, and materials evolution under process conditions. - Validate models against experimental measurements including wafer metrology, curvature and bow measurements, DIC, profilometry, nanoindentation, or failure analysis data. - Design and curate evaluation datasets in collaboration with RL researchers to train LLMs capable of directing complex simulation pipelines. - Generate simulated datasets for ML training in regimes where experimental coverage is expensive or difficult to achieve. - Build and automate simulation pipelines at scale, architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops. YOU WILL THRIVE HERE IF YOU HAVE - Periodic Labs is an early-stage startup, and we're looking for someone who can bring technical leadership to modeling structural and thermal behavior in semiconductor devices, not necessarily someone who already has every skill listed below. A strong growth mindset, demonstrated ownership, and a track record of getting up to speed quickly in new technical areas are much more important than experience in semiconductors. - A PhD or equivalent research experience in mechanical engineering, materials science, aerospace engineering, or a closely related field, with a strong foundation in solid mechanics and heat transfer. Early-career candidates with strong research or engineering output are encouraged to apply. - Hands-on experience with computational mechanics at the code level: writing or substantially modifying FEM codes, implementing constitutive models, or developing custom solvers for structural or thermal problems. - Familiarity with open-source simulation frameworks such as FEniCS, deal.II, MOOSE, or similar is a strong positive signal. Contributions to open-source projects that others actually use are even better. - Strong Python skills, with C++, Julia, or Fortran proficiency a plus. Experience running simulations programmatically at scale rather than through point-and-click tools. - Deep understanding of solid mechanics theory: continuum mechanics, tensor formulations, constitutive modeling, variational methods, and numerical methods for PDEs. - Experience using mechanics or thermal simulations to explain experimental observations, guide materials or process decisions, or surface failure mechanisms that were not obvious from measurement alone. - Genuine curiosity about AI and a desire to work at the boundary of simulation and machine learning. STRONG CANDIDATES MAY ALSO HAVE - Experience with GPU-accelerated FEM, reduced-order mechanical models, surrogate models, or physics-informed neural networks for structural problems. - Familiarity with multiscale mechanics: bridging from atomistic or DFT-informed potentials through molecular dynamics, mesoscale methods, and continuum mechanics. - Deep expertise in thin-film mechanics: residual stress, Stoney equation regimes and their limits, film-substrate interactions, delamination, and stress evolution during deposition. - Experience with wafer-scale mechanics: bow, warp, thermal cycling reliability, packaging-induced stress, or interconnect mechanics in advanced packaging contexts. - Background in fracture mechanics or damage modeling: LEFM, cohesive zone models, phase-field fracture, fatigue, or statistical failure models. - Knowledge of semiconductor metrology, failure analysis, or process integration that gives physical intuition for how models connect to real manufacturing decisions. 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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Senior Software Engineer, ML Ops

Negotiable

PathAI's mission is to improve patient outcomes with AI-powered pathology. PathAI is transforming traditional pathology methods into powerful, new technologies. These innovations in pathology can help accelerate drug development, improve confidence in the accuracy of diagnosis, and get life-saving therapies to patients more quickly. At PathAI, you'll work with a diverse and talented team of people, who are dedicated to solving complex problems and making a huge impact. We are seeking a highly skilled Senior Software Engineer (MLOps) . In this position, you play a key role in designing, developing, and scaling machine learning infrastructure that powers our enterprise AI systems. You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things work better. The Opportunity - You will architect and build infrastructure and automation, in AWS and on-premises, to support ML application development and deployment - You will drive system design and lead architectural discussions for our MLOps suite, ensuring it meets performance, security, and compliance requirements. - You will lead technical initiatives by researching, evaluating, and implementing new MLOps tools, frameworks, and best practices. - You will collaborate with machine learning engineers, data scientists, product engineering, and infrastructure teams to bridge the gap between research and production. - You will optimize ML workflows, ensuring models are efficiently and reproducibly deployed & monitored. - You will champion engineering excellence by enforcing high coding standards, conducting design reviews, and mentoring junior engineers. - You will automate ML operations, including CI/CD for ML models, feature engineering pipelines, and deployment strategies using Kubernetes, Airflow, and other orchestration tools. Who You Are: (Required) - You have a BS or Master’s in Computer Science, Computer Engineering, Software Engineering or closely related field. - You have 5+ years of software engineering experience , with a focus on building production-grade frameworks or applications - You have strong software engineering skills in complex, multi-language systems and experience with scalable backend architecture. - You have experience with Kubernetes and cloud computing platforms (AWS preferred). - You have experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog). - You have a solid understanding of DevOps principles and infrastructure-as-code (Helm, Terraform). - You have experience owning development platforms and serving internal customers - You have a solid level of proficiency in Python + exposure to additional languages Preferred: - You have experience with ML frameworks like PyTorch or Scikit-learn. - You have experience with data workflow orchestration frameworks (e.g., Airflow, Kubeflow). - You have expertise in MLOps principles , including model lifecycle management, feature stores, model monitoring, and CI/CD for ML. - You have experience with streaming data processing (Kafka, Flink, or Spark Streaming). - You have a solid level of familiarity with security and compliance best practices in ML systems. - You have experience using AI assistants (e.g. CoPilot, Cursor) in development. - You have outstanding interpersonal, verbal, and written communication and influencing skills: have built and cultivated important relationships both inside and outside of the organization and externally; have proven abilities to influence internal partners and stakeholders, thought leaders, national advocacy organizations, national standard-setting bodies, and other relevant external parties. - You have strong analytical and critical thinking skills with attention to detail; you have the ability to manage multiple projects and drive results in a fast-paced environment; you have a collaborative mindset with demonstrated leadership capabilities. This is a hybrid position based in Boston, MA. (A remote option may be considered for an exceptional candidate.) Relocation benefits are not available for this position. The expected salary range for this position based on the primary location Boston, MA is $127,500 - $195,500. 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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Senior Solutions Architect - Deep Learning

Negotiable

We are now looking for a Solution Architect - Deep Learning based in Israel. The individual will collaborate with brand-new Agentic and Physical AI tools alongside Machine learning, Deep Learning, and Data analytics to improve workflows and develop groundbreaking solutions. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most dedicated individuals in the world working for us. If you're creative and autonomous, we want to hear from you! What You'll Be Doing: Assisting field business development in guiding the customer through the sales process for GPU Computing products, owning the technical relationship and assisting the customer in building innovative solutions based on NVIDIA technology. Be a motivating leader in integrating NVIDIA technology into HPC architectures to support scientific and engineering applications. Be an internal champion for Deep Learning and Data Science among the NVIDIA technical community. Develop demo solutions and showcase to the AI ecosystem. What We Need To See: B.sc or M.sc in Engineering, Mathematics, Physics, or Computer Science or equivalent experience 5+ years of experience in software development or extensive data analytics, CUDA experience highly desirable. Experience working with modern Deep Learning software architecture and frameworks including PyTorch, VLLM, Triton Proven Exposure to GPU technology and CUDA programming Demonstrable experience in strategy, research, communications. Capable of working in a constantly evolving environment without losing focus. Committed with strong analytical and problem solving skills. Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very complex projects. Direct experience participating in communities (expertly or as highly engaged developer). Ways To Stand Out From The Crowd: Technical knowledge of developer digital platforms and their trends. Experienced working with containers, Kubernetes and MLoPS tools Exposure to C, C++ and Python. Research background. Good presentation skills #deeplearning

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Senior Software Engineer, AIOps and Observability

Negotiable

We are looking for a highly skilled Senior Software Engineer to design and develop AIOps & Observability platforms at NVIDIA. The platforms are used by internal teams to monitor, diagnose, and optimize the products, millions of assets and services in cloud, on-prem, data centers, supply chain, and edge. You will work with a team of engineers, product managers, and partners to define the observability strategy, roadmap, and standard methodologies for NVIDIA. You will also mentor and coach other engineers on observability, machine learning, tools and techniques. What you will be doing: Lead the design, development, and deployment of AIOps & Observability platforms, including metrics, logs, traces, events, alerts, dashboards, and visualizations. Drive the technical vision and roadmap for AIOps and Observability initiatives, aligning with business goals and industry best practices. Collaborate with other teams and customers to understand their observability needs and provide solutions that meet their requirements and expectations. Establish and implement observability standards, guidelines, and processes across NVIDIA. Research, evaluate, and adopt new observability technologies and frameworks that can enhance user experience. Provide peer reviews to other engineers including feedback on performance, scalability, security and correctness. Work with Data scientists to implement machine learning models for anomaly detection, forecasting, and root cause analysis on logs, metrics, and events. Handle large volumes of data and ensure data quality, security, and compliance. Develop and operate scalable, reliable, and distributed systems that can handle high traffic and complex workloads. Develop AI agents and AI-native observability tools that help engineers detect, understand, and resolve production issues faster. Build agentic workflows that reason across logs, metrics, traces, events, alerts, topology, and incident history to support anomaly detection, forecasting, root cause analysis, automated debugging, and remediation recommendations. What we need to see: Bachelor’s degree in computer science and engineering, or related field, or equivalent experience. 12+ years of experience in product development and full stack engineering, with 5+ years of experience in developing and operating observability platforms and solutions, preferably in a cloud-native environment. Strong knowledge and experience with observability tools, such as Prometheus, Victoria Metrics, Vector, Loki, Grafana, Alert Manager, Clickhouse, OpenTelemetry, etc. Hands-on knowledge in AIOps tools such as BigPanda, PagerDuty, Datadog, etc. Experience with Kubernetes, Nomad, Docker, and microservices architectures as well as experience with streaming services to ingest billions of events using NATS, Kafka, etc Proficient in one or more programming languages, such as Go, Python, Java, C#, etc. Passionate about observability and delivering high-quality internal platforms. Experience with developing Observability solutions to monitor On-prem and Public Cloud environments. Experience with running large Observability platforms on BareMetal Infrastructure Establish scalable data pipelines and instrumentation for collecting, aggregating, and visualizing telemetry and operational metrics. Ways To Stand Out From The Crowd: Deep understanding of implementing Observability solutions to large scale on-prem Infrastructure and Networking. Hands-on experience with managing large scale Observability Platforms with LLMs & ML Models and building custom services to ingest billions of metrics and logs from wide range of assets. Developed unified cloud observability platform to monitor Network, Compute, Power, Storage, Operating Systems, Security, Applications, SaaS Platforms. Demonstrated experience and expertise in using machine learning and Generative AI to develop solutions such as predictive monitoring, incident diagnosis, summarization and correlation. Demonstrate proficiency in AI/ML systems, generative AI, or agentic AI frameworks. 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, self-motivated and enjoy 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 200,000 USD - 322,000 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 4, 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.

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PhD Research Intern, AI Accelerator Design and VLS...

Negotiable

NVIDIA Research is seeking PhD-level students to work as research interns at the intersection of AI HW/SW Co-Design, Hardware Accelerator Architecture, IC Design Methodology, and VLSI Design. Ideal candidates will have a broad perspective across areas including machine learning fundamentals, quantization and numerical methods for machine learning model optimization, digital VLSI circuits for computer arithmetic, high-productivity VLSI design and verification methodologies including applications of agentic AI to hardware design, AI hardware micro-architecture, and VLSI methodology and implementation. What You’ll Be Doing: Accelerator Design: Contribute to novel research advancing the state-of-the-art in the design of accelerators for AI and other high-impact workloads. VLSI: Research creative and innovative ASIC and VLSI design techniques and/or novel digital VLSI circuits. Apply machine learning, agentic AI, and state-of-the-art tools and methodologies to automated ASIC and VLSI design tool flows. AI HW/SW Co-Design: Research and develop creative and innovative numerical methods for quantization, sparsity, or tensor decomposition grounded in computer arithmetic fundamentals and digital VLSI circuits. Collaborate on the development of research prototype testchips. Collaborate with AI researchers and hardware team members in research and product teams. Plan to publish and present your original research. What We Need To See: Pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field. Publication records in leading ML, architecture, VLSI, or circuits conferences. Excellent programming skills in Python/PyTorch and hardware design languages such as SystemVerilog/C++; Experience with High-Level Synthesis (HLS) tools is a plus. VLSI Implementation Skills: Experience in hardware design with proficiency with modern EDA tool flows; Tapeout experience is a plus. Excellent self-motivation, a high degree of creativity, and a passion for research, collaboration skills, and the ability to work effectively within a research team. Excellent written and verbal communication skills, with proven experience communicating technical work (e.g., academic presentations, poster sessions); ability to synthesize and explain complex technical concepts. Widely considered to be one of the technology world’s most desirable employers, NVIDIA has some of the most forward-thinking and hardworking people in the world inventing the future for us. Are you a creative and collaborative researcher interested in seeking new challenges? If so, we want to hear from you! Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 38 USD - 94 USD. You will also be eligible for Intern benefits . Applications for this job will be accepted at least until October 3, 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.

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Engineering Manager, Data Labeling Platform

Negotiable

NVIDIA is looking for an exceptional Engineering Manager to lead, scale, and innovate our core Data Labeling Platform. This is a highly visible, high-impact role where you will bridge the gap between bleeding-edge AI engineering, scalable software systems, and massive-scale operations. In this role, you will lead a team of highly talented engineers to design and build next-generation data annotation platform. The team develops and manages software that supports a high volume of active annotation projects across diverse research areas, like Nemotron, Cosmos, Robotics and Red teaming delivering a large quantity of annotations through NVIDIA's internal data operations and external annotation partners. Your team’s focus will be driving operational efficiency through annotation interfaces, scalable backend workflows, models-in-the-loop, auto-labeling systems, data pipelines, and intelligent orchestration tools. We are looking for a leader who can thrive across a wide spectrum of experience. Whether you are a seasoned Engineering Manager looking to take on an expanded scope, an entry-level Manager looking to solidify your leadership footprint, or a Principal/Staff Engineer (IC) with deep architectural roots ready to transition into people management, we want to hear from you. What you'll be doing: System Design & Programming: Maintain a high technical bar. You will remain close to the code, guiding robust software design, ensuring clean data engineering practices, and occasionally jumping into hands-on Python programming when solving complex architectural bottlenecks. People Leadership: Build, mentor, and lead a high-performing team of software, data, and AI application engineers. Foster a culture of technical excellence, accountability, and continuous growth. Stakeholder Management: Serve as a critical bridge and strategic partner, aligning engineering roadmaps with high-level VPs, Research Leaders, and our Data Factory operations workforce . AI Application Engineering: Architect and drive the implementation of next-generation auto-labeling applications that leverage multi-modal models-in-the-loop to dramatically reduce human labeling latency. Data Engineering & Analytics: Own the data engineering layer that makes annotation work measurable: event logging, ETL into NVIDIA's data lake, the metrics, dashboards, and alerting built on it against defined reliability and latency targets Front-End & Annotation Interfaces: Direct front-end engineering for custom annotation interfaces across text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. Operations Scaling: Optimize the platform for maximum scalability, data integrity, and throughput, ensuring the interface between human annotators and machine learning systems is seamless. What we need to see: Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field (or equivalent experience). 10+ overall years of professional software engineering experience, including 2+ years as a technical lead or engineering manager Hands-on Engineering: Strong background as a Software Engineer, Data Engineer, or AI Application Engineer, with excellent system design skills and deep hands-on expertise in Python . AI & Data Engineering: Proven experience in architecture-level understanding of data pipelines, distributed systems, and integrating machine learning models into production workflows (specifically auto-labeling or human-in-the-loop paradigms). Leadership Capability: Experience leading technical initiatives, mentoring engineers, or formally managing a team. We welcome senior individual contributors (ICs) with demonstrated tech-lead experience who are ready to make the leap to formal management. Exceptional Communication: Exceptional stakeholder management skills. You must be comfortable translating deeply technical constraints into strategic updates for VPs, while translating high-level business goals into operational directives for the Data Factory workforce. Ways to stand out from the crowd: Experience with human-in-the-loop data programs - managing data labeling platforms for RLHF or preference data or red teaming, supporting test, vision or multi-modal datasets. Experience applying models to reduce or assist human effort in a labeling workflow, such as automated pre-labeling, model-based quality evaluation, or agent-driven internal tooling, with measurement to back it up. Working proficiency in TypeScript or JavaScript, and experience delivering applications where interaction design materially affects user throughput and error rate. Background in multimodal data: video, 3D and point cloud, speech, or document understanding. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 200,000 USD - 322,000 USD for Level 3, and 248,000 USD - 391,000 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 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.

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Director, FSI Predictive Technology

Negotiable

We are looking for a Technical Fraud Director to define and guide the technical direction for scalable fraud technology and platforms. This role includes developing reusable technical capabilities that customers use to build, customize, and operate fraud detection, prevention, investigation, and decisioning systems. This role sits at the intersection of fraud prevention, machine learning, data engineering, security, risk, and distributed systems. You will collaborate with Engineering, Data Analysis, Protection, Risk Management, Product Development, Client Engineering, and Operations teams to translate evolving fraud patterns into production-grade fraud technology for customer use. You will help shape the technical foundation customers use to develop fraud systems that identify emerging threats at scale, improve detection quality, reduce false positives, and respond faster to adaptive fraud behavior. If you feel you are an engaged technical expert able to navigate architecture, data, modeling concepts, system building, customer needs, and multi-functional coordination, please apply today! What You’ll Be Doing: Lead the technical strategy, architecture, and roadmap for fraud technology built to scale and support customers in building, customizing, and operating fraud detection, prevention, investigation, and decisioning systems. Design reusable detection approaches that combine rules, machine learning, anomaly detection, behavioral analytics, graph analytics, entity resolution, and risk scoring. Partner with Data Science, ML Engineering, Product, and Customer Engineering teams to develop, evaluate, deploy, and continuously improve fraud detection capabilities for customer use cases. Identify, prioritize, and integrate fraud signals across transactional, identity, account, device, network, application, behavioral, and operational data. Establish frameworks for rapidly translating newly discovered fraud patterns into production rules, signals, models, and detection workflows that can be adopted across customer environments. Define and monitor detection effectiveness using metrics such as precision, recall, false-positive rates, detection coverage, alert quality, latency, and business impact. Lead technical root-cause analysis when fraudulent activity bypasses existing controls and drive improvements to detection logic, data coverage, and system resilience. Build reusable detection infrastructure, services, APIs, reference architectures, and frameworks that support multiple products, fraud types, customer environments, and business use cases. Evaluate emerging technologies and determine where AI, machine learning, graph analytics, automation, and analyst-assist tooling can improve fraud detection and response. Lead technical build and architecture reviews, driving alignment across Development, Data Science, Security, Risk, Product, Customer Engineering, and Service Delivery collaborators. What We Need to See: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent experience. 15+ years of progressive experience in software engineering or a related technical discipline, including 6+ years of experience leading and managing complex, cross-functional engineering organizations and delivering high-impact technical products or platforms. Deep expertise in one or more of the following areas is required: software engineering, fraud technology, risk systems, security engineering, machine learning, data science, or data engineering. Significant experience designing, building, or operating large-scale fraud, abuse, risk, security, detection, or machine learning systems. Experience developing platforms, products, APIs, services, or technical frameworks that are adopted by internal or external customers to build production systems. Strong understanding of detection methodologies, including rules-based, statistical, behavioral, anomaly-based, graph-based, and machine-learning approaches. Experience designing real-time, high-volume, distributed, or event-driven data processing systems. Experience with data pipelines, feature engineering, model inference, production ML systems, or decisioning platforms. Demonstrated ability to identify meaningful signals within large and complex datasets and translate them into actionable detection capabilities. Experience defining metrics and using data to evaluate and improve detection-system effectiveness. Strong systems-thinking skills and the ability to turn ambiguous fraud, abuse, customer, or threat patterns into clear technical requirements and scalable solutions. Demonstrated experience leading complex technical initiatives across multiple engineering, data, risk, product, and customer-facing teams. Ways to Stand Out from the crowd: Deep experience with machine learning-based fraud detection, anomaly detection, behavioral modeling, entity risk scoring, or adaptive risk systems. Experience with graph analytics, graph machine learning, entity resolution, link analysis, or identifying coordinated activity across complex networks of entities and developing systems that detect adaptive or adversarial behavior where attack patterns change in response to existing controls. Experience applying generative AI or LLMs to fraud detection, investigations, threat analysis, case summarization, or analyst workflows and designing fraud technology platforms that support multiple products, organizations, geographies, customer environments, or fraud use cases rather than individual point solutions. Experience designing low-latency inference, streaming, event-processing, or real-time decisioning systems. Experience developing automated feedback loops that use confirmed fraud, investigation outcomes, customer disputes, chargebacks, or analyst decisions to improve models and detection logic. NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction — from artificial intelligence to autonomous vehicles. NVIDIA is widely considered one of the technology world's most desirable employers. We have some of the most forward-thinking and hard-working people in the world working for us. If you're passionate about building the infrastructure that runs AI at scale, we want to hear from you. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 26, 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.

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Applied Machine Learning Engineer, AI for VLSI Des...

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. Our team builds AI-driven software systems for Circuit Design: combining automation algorithms, DL models and agentic workflows to accelerate end-to-end design automation. Come join this integral team in our Circuit Solutions Group! What you'll be doing: Work within a multi-functional team on various projects involving Pre-silicon and Post Silicon custom circuit design and related data, Circuit/Layout Optimization and Spice correlation. Research and implement techniques on frontier solutions of electronic design automation. Build and innovate agentic AI solution for VLSI design problem. Responsible for analyzing the problem or datasets, raise and validate hypotheses, design and build models and algorithm until they reach the desired QOR. What we need to see: MS or PhD in Electrical/Computer Engineering degree (or equivalent experience). Experience in the following fields is a strict requirement for this role: Combinatorial Optimization, Agentic AI and large language models, Machine Learning for Chip Design & EDA Background in Algorithms/Data Structures. Experience in Applied Math/Machine Learning/Software programming with shown ability in writing code in Python, C++. Ways to stand out from the crowd: Prior background in large-scale EDA software development is a plus. Prior experience in CMOS layout drawing, including schematic-to-layout translation and DRC/LVS compliance, is a definite plus. Enjoy working with multiple levels and teams across organizations (engineering/research, product, sales and marketing teams). Effective verbal/written communication, and technical presentation skills. NVIDIA is a pioneer in bringing groundbreaking technology to new markets. We have some of the most forward-thinking and hardworking people in the world working with us. If you're creative and autonomous, we want to hear from you! #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 116,000 USD - 184,000 USD for Level 2, and 152,000 USD - 230,000 USD for Level 3. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 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.

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Software Engineer, AI Platform - New Grad

Negotiable

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role Our software team is growing, and we are looking for talented engineers who are graduating by December 2026 to join us and be instrumental to one of the following areas: Data Platform, Onboard Systems, ML Infrastructure, Simulation, or Technical Infrastructure teams. About the Work Depending on your skill set and areas of interest you will work on some or all of the following: - Data Platform: The Data Platform serves as a comprehensive management system for Nuro AI Driver's data, labels, and metrics, facilitating seamless access functionality. The team focuses on data annotation across various domains, including 2D/3D perception, mapping, behavior trajectory, and language/text. It also handles data ingestion and mining, employing methods such as heuristics and embedding search. Additionally, the platform supports the autonomy evaluation infrastructure by providing detailed introspection. - Onboard Systems: Our onboard system team’s software engineers provide a reliable and high-performance platform that allows our autonomy teams to integrate their autonomy software and algorithms that work across various self-driving platforms. This work requires close collaboration with our software teams, hardware teams, and systems/safety team to make sure new software and hardware work together safely and reliably and resolve onboard error and performance problems. - Technical Infrastructure: this group owns few fundamental services for entire engineering organizations: generic compute platform to host mission-critical workflows such as data processing and simulation, storage management service which manages hundreds of PB of data, cloud infrastructure serves as IaaC which provisions and maintains all cloud resources, engineering productivity provides tools such as build and CI/CD to make engineering work more efficient. About You You have deep expertise and prior experience in some or many of the following areas: - You are a current Bachelor’s Degree or Master’s Degree candidate in Computer Science, Electrical Engineering, Robotics, or related field, graduating before December 2026. - You have experience in one or more of the following areas: large-scale distributed systems; data storage and processing systems; advanced algorithms using C++ and Python; machine learning, multithreading; x86 architecture; and software performance tuning and optimization, robotics software frameworks, different compute modalities (CPU, GPU, FPGA) etc. - You have strong problem-solving and programming skills. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $153,000 to $176,000 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.

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Senior/Staff Software Engineer, ML Data Infrastruc...

Negotiable

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable data infrastructure. This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs. Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment. About the Work - Design and develop unified, introspectable, large-scale batch and streaming data pipelines that can ingest and process data across a wide range of use cases relevant to evaluation. - Create and implement a storage system capable of accommodating both the large volume and diverse range of evaluation and performance metrics. - Construct intuitive dashboards and reports to present evaluation results, facilitating straightforward comparisons that highlight both improvements and regressions of the ML components and the overall system. - Develop and maintain continuous testing and monitoring systems to guarantee the integrity and resilience of our data and associated data pipelines. - Develop data mining tools with applied ML techniques to support data discovery needs from Autonomy including Perception, Behavior, and Mapping - Develop data annotation tools to support first-party and third-party labeling workforce to provide high fidelity perception, mapping, and driving trajectory labels - Scale data annotation labels with applied State-of-the-art ML techniques About You - You have a degree in BS, MS.c or Ph.D, plus 4 years of relevant work experience - Strong proficiency in Python or similar languages - Domain experience: Experience working with large-scale data and building scalable & reliable systems/data pipelines; ability to understand and design complex systems - Engineering leadership: Experience setting team or project product and technical vision, timelines, and prioritization; being a Technical Lead, mentoring and support junior engineers - Technical excellence: Ability and willingness to deep dive into implementation, driving technical standards and best practices across broader software organization - A bachelor's degree in Computer Science, Electrical Engineering, or a closely related field Bonus Points - Strong proficiency in C++ or other high-performance low-level languages - Strong knowledge of GCP, GCS, BigQuery, or PostgreSQL - Knowledge of data engineering, and its tooling and best practices - Knowledge of batch and streaming data processing, warehousing, and analytics solutions - Experience working with large-scale distributed data systems - Experience with system & framework design - Experience with data workflow orchestration platforms At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected pay range is between $193,930 and $352,290 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.

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Senior Software Engineer, Perception ML Data

$193,930 – $291,150

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role We’re a team of high-output generalists where ML and systems engineering converge. This is not a "run the models" role. We reason from first principles about why a perception model learns what it learns, close the gaps that cap its performance, and raise the bar on the data and evaluation loop that drives autonomy. Your work will directly impact how autonomous systems understand rare scenarios, adapt to global geographies, and scale safely. About the work You’ll solve autonomy’s hardest data challenges through applied ML and systems rigor: - Diagnose why perception models underperform on the long tail, and turn that into targeted data and training priorities. - Design eval metrics and regression detection that tell us whether a model is ready. - Curate and clean training data for segmentation and occupancy; hunt the data problems that silently cap performance. - Run controlled ML experiments and ablations; cleanly separate data problems from model problems. - Debug the full data → train → eval → autonomy path, across ML and infra boundaries. About You - BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area. - 4+ years of industry software engineering experience with Python fluency and C/C++ familiarity. Proven ability to lead cross-functional technical projects from design to completion. - You've implemented core ML from first principles (not only called framework APIs), and can reason about why a model learns what it does. - Hands-on perception experience with detection, segmentation, occupancy, 3D, or sensor data. - You're interested in solving data problems. Bonus Points - Occupancy / BEV; sensor modalities (lidar, camera, radar). - Familiarity working with synthetic or autonomous driving data. - Experience building ML systems for robotic applications At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected pay range is between $193,930.00 and $291,150.00/year for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP

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Director of Engineering, Eval Platform

Negotiable

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role The Eval Platform team owns the simulation and evaluation and validation platform that underpins autonomy development and driverless readiness validation. This platform is mission‑critical for both rapid iteration during development and rigorous validation used to assess safety, performance, and deployment readiness. We are looking for a seasoned engineering leader to lead the Eval Platform as a Director‑level role. This leader will define the technical vision, execution strategy, and organizational structure for a multi‑disciplinary org spanning simulation, evaluation infrastructure, and compute and storage platform. The evaluation platform brings together data, simulation, metrics, and analysis workflows into a coherent system that allows the teams to: - Iterate quickly to improve the autonomy performance - Measure autonomy performance and risk with credibility and rigor - Make confident, data‑driven decisions about driverless deployment About the Work - Define and own the technical vision and roadmap for the Eval Platform, spanning simulation, evaluation infrastructure, compute and storage platform . - Lead, mentor, and grow a multi‑team organization (managers and senior ICs), fostering a culture of technical excellence, ownership, and continuous improvement. - Build and scale simulation and eval capabilities that support both rapid autonomy iteration and rigorous validation use cases. - Own the autonomy evaluation and validation platform, and partner closely with Autonomy and Systems leadership, ensuring it is reliable, scalable, and trusted by autonomy engineering, validation, and leadership - Represent Eval Platform at the executive level, clearly communicating tradeoffs, progress, risks, and readiness About You - Deep technical expertise in building simulation systems, evaluation platforms for large‑scale ML / robotics systems - Strong understanding of autonomy and validation, and how to build high quality tools for these use cases. - Proven experience leading and scaling high‑impact engineering organizations, including managers and senior ICs - Ability to translate complex technical signals into clear, actionable insights for senior leadership - Comfortable operating in a fast‑paced, high‑ambiguity environment At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $258,000 and $387,000 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP

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Forward Deployed Engineer

Negotiable

NORMAL COMPUTING | BUILD WITH US Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. THE ROLE The cost of taping out silicon is enormous, and the complexity of verification makes multiple tapeouts hard to avoid. Normal EDA accelerates this work as an AI platform for collaborative silicon engineering: a single source of truth across the chip lifecycle, learning continuously from the teams that use it. As a Forward Deployed Engineer, you own our EDA system inside a customer's environment. Embedded directly with our partners, you adapt our platform to their data, workflows, and design challenges, working alongside our account executive and a deployment strategist to make the deployment a success. You thrive as a problem-solver and take pride in winning over customers along with the rest of your team. You will be debugging distributed systems, building new product features, post-training models, and working in various silicon-native languages such as SystemVerilog. Note that many different kinds of candidates could be well-qualified for this role, even with non-overlapping expertise (e.g. ML background vs. silicon background). WHAT YOU WILL OWN - Production Problem-Solving: Diagnose issues in our system, the model, the data, or the workflow. Work deep in both Normal's systems and the customer's environment to resolve them, and close the loop with their engineers. - Evaluation Against Reality: Design and run evals against real customer workflows, validating generated artifacts against their specifications so model behavior holds up in production. - Platform Integration: Integrate the platform with each customer's data, design flows, and tooling, working with their production codebases and against their existing infrastructure. - Customer Signal: Embedded with silicon design teams, translate their constraints into model and platform requirements, and carry that signal back to Normal's research, product, and platform teams to shape what gets built next. - Continual Learning: Post-train Normal's models on-prem on proprietary customer data and trajectories to customize to their workflow, tooling, and style preferences. Build the continual-learning loops that turn their engineers' feedback into system knowledge, so model quality compounds across the engagement. - Judgment Ahead of Playbook: Make the calls on what to build, what to skip, and when to push back on a request that would compromise what ships. Codify what works into patterns that raise the floor for every engagement after yours. WHAT MAKES YOU A GREAT FIT - Great at problem-solving and tracking down issues wherever they are in the stack - Strong software engineering fundamentals: proficient in Python, comfortable in production codebases, distributed-systems literate - Hands-on experience with the modern ML stack: prompt engineering, fine-tuning, evals, agentic patterns, model deployment - Willingness and ability to go deep on semiconductor verification workflows. You will spend significant time inside UVM testbenches, SystemVerilog codebases, and design specifications. Prior experience is a strong advantage, but what matters is whether you can build fluency fast and earn credibility with verification engineers - An ability to ship ML systems inside customer or production environments where model behavior had to hold up against real-world data - Calm in ambiguity: you make good decisions with incomplete information, and you know when to act and when to ask - Comfortable with travel when needed; anywhere between a few days for customer meetings and a few months for longer-term customer projects BONUS POINTS - Direct experience with EDA, semiconductor design flows, verification workflows (UVM, SystemVerilog, coverage-driven verification), or other areas of silicon engineering - Built or led an FDE or customer-deployment function from the ground up at an earlier-stage company - Open-source contributions or publications in AI or ML venues Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com. Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice https://normalcomputing.com/applicant-privacy-notice.

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Agentic Engineering Platform Engineer

Negotiable

The AI orchestration of your wildest imagination. n8n is the open workflow orchestration platform built for the new era of AI. We give technical teams the freedom of code with the speed of no-code, so they can automate faster, smarter, and without limits. Backed by a fiercely inventive community and 500+ builder-approved integrations, we’re changing the way people bring systems together and scale ideas for impact. Since our founding in 2019, we’ve grown into a diverse team of over 260 - working across Europe and the US, connected by a shared builder spirit and with our centre of gravity in Berlin. Along the way, we’ve: - Cultivated a community of more than 650,000 active developers and builders - Earned 190K+ GitHub stars, making us one of the world’s Top 40 most popular projects - Backed by top investors, from Sequoia’s first German seed to our SAP's recent strategic investment - bringing us to a $5.2bn valuation That’s the company we’ve built. Now we’d love to see what you can build. If you’re applying, try n8n out - whether you’re technical or not - and share a screenshot of your first workflow with us. The easiest place to start is here: app.n8n.cloud/register http://app.n8n.cloud/register. We’re in a defining moment of an incredible journey. Come and build with us. AGENTIC ENGINEERING PLATFORM ENGINEER Developer Productivity · AI Coding Agents · Developer Platform 🎯 Your main goal will be to keep n8n operating as an AI-native engineering organization where agents are embedded across the development cycle; helping teams plan, build, test, review, learn, and improve. You’ll do this by maintaining and evolving the platform foundations, tools, guardrails, and team processes that make agentic development safe, measurable, and sustainable. Working with Developer Platform, Security, AI/Agents, Architecture, and product engineering teams, you’ll help teams continuously adopt what works from frontier AI and make success visible through faster feedback loops, better quality, stronger resilience, and more learning from what we ship: BUILD THE PAVED ROAD FOR AGENTIC DEVELOPMENT - Define safe task categories for autonomous agents, including what they can attempt, what they should never touch, and where additional review is required. - Design and implement isolated execution environments where agents can work without exposing secrets or accessing sensitive systems unnecessarily. - Build pilot workflows that allow selected Linear issues to be picked up by agents, tested, and converted into useful draft PRs. MAKE N8N'S MONOREPO AGENT-READY - Create and maintain repo instructions, agent playbooks, ownership metadata, coding guidelines, and documentation that improve agent performance. - Continuously improve developer workflows, repo structure, testability, and documentation based on where agents succeed, fail, or create reviewer friction. - Partner with Architecture and senior engineers to define boundaries, dependencies, and areas where agents need more context or tighter constraints. INTEGRATE AGENTS INTO ENGINEERING WORKFLOWS - Integrate agentic workflows with Linear, GitHub, CI, test infrastructure, and developer review processes. - Design PR evidence templates that help reviewers understand what changed, why, what tests ran, what risks remain, and where human judgment is needed. - Partner with Security, AI/Agents, Developer Platform, and product engineering teams to define guardrails for sensitive areas like auth, billing, permissions, and data handling. MEASURE QUALITY, SAFETY, AND ADOPTION - Create benchmarks from real historical engineering tasks to evaluate agent output quality, failure modes, test pass rates, review burden, and production risk. - Define quality signals and adoption metrics such as draft PR usefulness rate, reviewer acceptance rate, CI pass rate, rework rate, and developer satisfaction. - Use data and feedback from pilot teams to improve agent workflows, reduce unsafe attempts, and build trust across engineering. REQUIREMENTS MUST-HAVES - 🧠 Autonomous coding agents: You have experience with autonomous coding agents, AI coding platforms, agent orchestration systems, or internal AI developer tooling. - 🤖 Agentic development experience: You have practical experience building, deploying, or operating AI-assisted or agentic software development workflows that work with developers not besides them. - 🛠️ Strong engineering foundations: You have a strong software engineering background, ideally in platform engineering, developer experience, infrastructure, tooling, CI/CD, or engineering productivity. - 🧭 Ambiguity leadership: You’re comfortable leading a first-of-a-kind technical initiative from problem definition through pilot, adoption, measurement, and iteration. - 🔁 Modern engineering workflows: You deeply understand GitHub, CI, testing strategies, code review, monorepos, issue tracking, developer environments, and release risk. - 🔐 Safety-first judgment: You have strong judgment around permissions, secrets, sandboxing, sensitive code paths, and human-in-the-loop review. - 📊 Measurable systems thinking: You know how to design benchmarks, quality signals, failure-mode tracking, and adoption metrics. - ✍️ Clear written communication: You’re strong at writing repo instructions, playbooks, PR evidence templates, technical proposals, and alignment documents. - 🤝 Cross-functional influence: You have credibility with senior engineers and can influence across Platform, Product Engineering, AI, Security, and Architecture without formal authority. NICE-TO-HAVES - 📦 TypeScript monorepos: You have worked in a large TypeScript, Node.js, or JavaScript monorepo. - ⚙️ Developer platforms: You have experience with GitHub Actions, Linear, automated testing infrastructure, preview environments, or internal developer platforms. - 🛡️ Security-aware platform engineering: You have worked on sandboxing, secrets management, least-privilege access, or sensitive code ownership. - 📏 AI evaluation: You have created evaluation frameworks, benchmarks, or quality metrics for AI-generated code or automation systems. - 🌍 Open-source communities: You have experience contributing to open-source or source-available projects with active developer communities. - 🏢 B2B SaaS context: You understand environments where reliability, permissions, data handling, and customer trust are central. - 🔗 Workflow automation: You’re familiar with workflow automation, integration platforms, AI orchestration, or developer-facing products. Why join us? At n8n, you’ll help define how autonomous AI agents become a trusted part of engineering inside one of Europe’s fastest-growing AI-native infrastructure companies. Your work will shape the platform foundations, safety model, evaluation framework, and developer workflows that determine whether agents can contribute meaningfully to production engineering without lowering the quality bar. You’ll join a remote-first team with high autonomy, high visibility, and a strong builder mindset. Because this is a first-of-a-kind role, you’ll have the opportunity to set direction, influence engineering culture, and create learnings that may inform both internal engineering productivity and n8n’s external product thinking. Sound like a challenge you’re excited to take on? Apply now — and help us build the future of automation. n8n is an equal opportunity employer and does not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status. We can sponsor visas to Germany; for any other country, you need to have existing right to work. Our company language is English. You care about diversity and inclusion? We do too! Check out our Diversity, Inclusion and Belonging initiatives at n8n https://www.notion.so/Diversity-inclusion-and-belonging-n8n-c1bec2fff536422d868b1a438d990e35?pvs=21 (https://www.notion.so/n8n/Diversity-inclusion-and-belonging-n8n-c1bec2fff536422d868b1a438d990e35). Location disclaimer: If you see multiple job postings for the same role, it is most likely because we're hiring remotely for this role and posting in different locations to make sure every potential candidate can see the role. Please apply to the location you're the most likely to work from in the future. BENEFITS - Competitive compensation 💸 – We offer fair and attractive pay. - Ownership 💪 – Our core value is to “empower others,” and we mean it—you’ll get a slice of n8n with equity. - Work/life balance 🏖️ – We work hard but ensure you have time to recharge: - Europe: 30 days of vacation, plus public holidays wherever you are. - US: 20 vacation days, 8 sick days, plus public holidays wherever you are. - Health & wellness 🩺 – - Europe: We provide benefits according to local country norms.* - US: Multiple low-premium, low-deductible medical plans with coverage for individuals and families—plus a no-cost premium HDHP option with a pre-seeded HSA—along with dental and vision coverage. - Future planning 💰 – - Europe: We provide pension contributions according to local country norms.* - US: 401(k) retirement plan with a 4% employer match. - Financial security 🛡️ – - Europe: We provide benefits according to local country norms.* - US: Company-paid short-term and long-term disability insurance, plus life insurance to support you and your loved ones. - Career growth 📈 – We hire rising stars who grow with us! You’ll get €1K (or equivalent) per year to spend on courses, books, events, or coaching to level up your skills. - A passionate team 🤩 – We love our product, and we prove it with regular hackathons where we see who can build the coolest thing with it! - Remote-first 🌏 – Our team works remotely across Europe, with regular off-sites for team bonding. Some roles, like sales in the US, are hybrid—please check the job description. - Giving back 🤝 – We're big fans of open source, and you'll get $100 per month to support projects you care about. - Transparency 🙏 – We all know what everyone’s working on, how the company is doing—the whole shebang. * Country-specific details are provided in your contract.

M

Developer Advocate, MAX Inference & Serving

Negotiable

About Modular At Modular, a Qualcomm company , we’re on a mission to revolutionize AI infrastructure by systematically rebuilding the AI software stack from the ground up. Our team, made up of industry leaders and experts, is building cutting-edge, modular infrastructure that simplifies AI development and deployment. By rethinking the complexities of AI systems, we’re empowering everyone to unlock AI’s full potential and tackle some of the world’s most pressing challenges. If you’re passionate about shaping the future of AI and creating tools that make a real difference in people’s lives, we want you on our team. You can read about our culture and careers to understand how we work and what we value. About the role: We are looking for a Developer Advocate to evangelize the MAX Platform's inference and serving capabilities with our user base and developer community. This involves creating technical content such as user guides and blog posts as well as giving talks at conferences, leading workshops, all with the goal of enabling our community of builders deploying models in production. Join our world-leading product team and be part of redefining how AI infrastructure is built and deployed. LOCATION: Candidates based in the US or Canada are welcome to apply. To support growth and collaboration, those in earlier career stages work in a hybrid capacity at our Los Altos, CA. More senior staff can work out of our office in Los Altos, CA or remotely from home. Onboarding for new hires is conducted in-person in our Los Altos, CA office. Additionally, this role requires travel to conferences and developer events, which may be as often as once per month, as well as travel for team and company events (typically 2-4 times per year). What you will do: Build and publish reproducible benchmarks comparing MAX against vLLM, SGLang, TensorRT-LLM, and Triton Inference Server, including methodology, harness code, and hardware configurations so others can verify the numbers. Deploy and profile real inference workloads on MAX across CPU and GPU targets, and investigate performance gaps in latency, throughput, and cost per token. Write and maintain technical content grounded in that work: performance deep dives, serving architecture explainers, and posts that show how MAX handles batching, KV cache management, quantization, and multi-GPU serving. Author runnable tutorials, examples, and video walkthroughs covering model deployment on MAX, from pip install modular to a served endpoint under load. Provide technical support to engineers evaluating MAX for inference and serving, including teams at some of the world's largest companies, and debug their deployment and performance issues directly. Answer technical questions across GitHub, Discord, X, and LinkedIn, reproducing reported issues and filing them with enough detail for engineering to act on. Translate community and customer findings into prioritized inference and serving feedback for engineering and product, shaping the MAX roadmap. Present technical talks, benchmark results, and live demos at conferences, summits, and meetups. Help set the technical direction of Modular's developer relations content, including what gets benchmarked, documented, and demoed next. What you bring to the table: 3-5 years of professional engineering experience, with at least some of it spent deploying or operating ML systems in production. You have run inference workloads yourself, not just written about them. Deep familiarity with the ML inference stack. You can explain continuous batching, KV cache management, quantization trade-offs, and tensor and pipeline parallelism, and you have hands-on experience with at least one of vLLM, Triton Inference Server, TensorRT-LLM, or SGLang. Strong Python and systems programming experience in C++, Rust, Mojo, or CUDA is a significant advantage, especially if you have profiled and optimized GPU code. Comfort with the deployment surface around serving: containers, Kubernetes, GPU drivers and runtimes, and the usual ways a cluster refuses to cooperate. You benchmark rigorously. You know why one number is not a result, how to control for warmup and batch size, and when a comparison is not apples to apples. You write well about technical work, and you have a portfolio to show it: blog posts, tutorials, videos, docs, or courses. You explain complex ideas without losing precision, and you cut the filler. You learn new tools fast and turn them into accurate content quickly. A feature ships Tuesday, your tutorial goes out Thursday and the code runs. A growth and leadership mindset, with a collaborative attitude that seeks to learn more from our customers, team members, and the broader market Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or technical related field. 2+ years of Product Management or related work experience. *Completed advanced degrees in a relevant field may be substituted for up to two years of work experience. Helpful, but not required: Experience programming GPUs using CUDA or ROCm. Familiarity with the Mojo 🔥 programming language and MAX AI framework. Familiarity with open source software development practices and communities. Experience producing high-quality videos covering technical topics. What Modular brings to the table: Amazing Team. We are a progressive and agile team with some of the industry’s best engineering and product leaders. World-class Benefits. In order to attract the best, we need to offer the best. Your benefits package may include comprehensive healthcare coverage, retirement and savings programs, employee stock purchase opportunities, paid time off, wellbeing resources, family support programs, and learning and development opportunities. Please note that specific benefit packages may vary based on your location, you can read more about benefits offered by Qualcomm here . Competitive Compensation. We offer very strong compensation packages, including RSU grants. We want people to be focused on their best work and believe in tailoring compensation plans to meet the needs of our workforce. Team Building Events. We organize regular team onsites and local meetups in Los Altos, CA as well as different cities. Traveling 2-4 times a year is expected for all roles. Working at Modular will enable you to grow quickly as you work alongside incredibly motivated and talented people who have high standards, possess a growth mindset, and a purpose to truly change the world. The estimated base salary range for this role to be performed in the US is $155,400.00 - $233,000.00 USD . The estimated base salary range for this role to be performed in Canada is $118,200.00 - $177,200.00 CAD . The salary for the successful applicant will depend on a variety of permissible, non-discriminatory job-related factors, which include but are not limited to education, training, work experience, business needs, or market demands. This range may be modified in the future. The total compensation for a candidate will also include annual target bonus, equity, and benefits, with equity making up a significant portion of your total compensation. For candidates who fall outside of the listed requirements, we nevertheless encourage you to apply as we may have upcoming openings that are lower/higher level than the ones advertised.

M

Principal Machine Learning Engineer (Reconstructio...

Negotiable

WHAT YOU’LL DO 1. Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy. 2. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements. 3. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks. 4. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation. 5. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. WHAT WE’RE LOOKING FOR - Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains. - Ability to move fluidly between research prototypes and production-quality systems. - Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility. - A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) USEFUL EXPERIENCE - ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints. - Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts. - A background in computational physics or scientific computing. - Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. - Experience in Agentic-SciML is a plus. - Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines. - Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

M

Software Engineer, Automation

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. About the Role As a Software Engineer on the Automations team at Mercor, you’ll join a small, high-leverage engineering group building the autonomous systems that run Mercor’s internal operations — and eventually, operations at scale for the companies we serve. This is not a research role. You’ll design and ship production agents that take real actions: scheduling interventions, executing operational playbooks, managing talent pipelines, and surfacing decisions that previously required hours of manual click-ops. From day one, you’ll own systems end-to-end — from MCP server design to evaluation frameworks — working directly with operators and platform engineers to turn repeatable human workflows into reliable, autonomous ones. You’ll reason about correctness, rollback, observability, and trust in systems that touch payments, contracts, and people’s livelihoods. This is greenfield agentic infrastructure with immediate real-world impact. What You’ll Do - Build and maintain MCP servers that give agents structured access to Mercor’s platform reading project state, taking management actions, and triggering communications. - Design and implement evaluation frameworks to measure agent decision quality and drive continuous improvement through feedback loops. - Develop action queues and playbook orchestration systems that allow agents to execute multi-step workflows with conditional logic. - Instrument agent systems end-to-end — capturing outcomes, closing feedback loops, and enabling hill-climbing on eval metrics. - Collaborate directly with operators to identify high-leverage automation opportunities and translate them into robust agent behaviors. - Debug and harden agent systems in production, designing for reliability at scale. - Contribute to foundational patterns and infrastructure that the rest of the company will build on. What We’re Looking For - Strong backend engineering fundamentals in Python (or equivalent modern language). - Experience building with LLM APIs — including tool use, structured outputs, and multi-step reasoning workflows. - Comfort designing evaluation frameworks and thinking rigorously about agent failure modes. - Ability to ship quickly and iterate on systems that interact with the real world. - Understanding of distributed systems, APIs, and production infrastructure. - Full-stack experience is a plus — owning features end-to-end will increase your impact. Why Mercor - Impact: Your work powers how the world’s leading AI labs train and test their models. - Learning: Get early insights into frontier model capabilities months before the market. - Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership. Benefits - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

M

Software Engineer, Applied AI - Frontier Engineeri...

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE On the Software Engineer, Applied AI - Frontier Engineering team , you’ll build, deploy, and operate systems that sit directly between frontier AI research and data delivery. This is a high-ownership, deeply technical role. You’ll work through ill-defined problems, prototype quickly with researchers and customers, and take systems from early experiments to reliable, scalable production. You’ll own projects end-to-end: spanning requirements gathering, creating data creation pipelines, and improving model-adjacent infrastructure, while partnering closely with frontier AI labs and Mercor’s internal teams to ship high-impact applied AI solutions. WHAT YOU’LL DO - Partner closely with frontier AI labs to understand their data, post-training, and evaluation needs - Build and operate scalable data pipelines for post-training workflows and model evaluations - Design and build scalable systems for synthetic data generation and data quality, and work directly with customers to understand requirements and develop technical solutions - Prototype new data types, benchmarks, and evaluation frameworks - Lead technical discussions with customers WHAT MAKES THIS ROLE DIFFERENT - Direct frontier exposure. You’ll work closely with researchers at leading AI labs, building infrastructure that directly accelerates cutting-edge research. - Coding + customer work. This role blends deep technical execution with customer interaction. Engineers who enjoy both building and communicating tend to thrive. DAY-TO-DAY - Fast-moving, high-ownership environment - Technically demanding, collaborative work - “Building the plane while flying it” - In-person culture at our new New York office at One World Trade Center - Aligned with frontier research timelines WHAT WE’RE LOOKING FOR - Strong backend engineering fundamentals in a modern language (Python, Go, Rust, etc.) - Experience with model training and inference - Strong grounding in statistical analysis and experimental design for measuring model performance and improvements - Familiarity with evaluation methods for large language models - Comfort working through ambiguity and shipping iteratively YOU’RE LIKELY SOMEONE WHO - Enjoys ownership and customer-facing problem solving - Thinks entrepreneurially and moves quickly - Balances speed with engineering rigor - Communicates clearly with technical and non-technical users WHY ENGINEERS JOIN - Direct exposure to frontier AI research - Real ownership and visible impact - Technical depth paired with human interaction - Fast feedback loops and high leverage - There are very few roles that combine this level of technical rigor, customer proximity, and research exposure. BENEFITS - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K proximity bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

M

Software Engineer, Applied AI - Frontier Engineeri...

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE As a Software Engineer on Applied AI, you’ll build, deploy, and operate systems that sit directly between frontier AI research and data delivery. This is a high-ownership, deeply technical role. You’ll work through ill-defined problems, prototype quickly with researchers and customers, and take systems from early experiments to reliable, scalable production. You’ll own projects end-to-end: spanning requirements gathering, creating data creation pipelines, and improving model-adjacent infrastructure, while partnering closely with frontier AI labs and Mercor’s internal teams to ship high-impact applied AI solutions. WHAT YOU’LL DO - Partner closely with frontier AI labs to understand their data, post-training, and evaluation needs - Build and operate scalable data pipelines for post-training workflows and model evaluations - Design and build scalable systems for synthetic data generation and data quality, and work directly with customers to understand requirements and develop technical solutions - Prototype new data types, benchmarks, and evaluation frameworks - Lead technical discussions with customers WHAT MAKES THIS ROLE DIFFERENT - Direct frontier exposure. You’ll work closely with researchers at leading AI labs, building infrastructure that directly accelerates cutting-edge research. - Coding + customer work. This role blends deep technical execution with customer interaction. Engineers who enjoy both building and communicating tend to thrive. DAY-TO-DAY - Fast-moving, high-ownership environment - Technically demanding, collaborative work - “Building the plane while flying it” - In-person culture (SF, 181 Fremont) - Aligned with frontier research timelines WHAT WE’RE LOOKING FOR - Strong backend engineering fundamentals in a modern language (Python, Go, Rust, etc.) - Experience with model training and inference - Strong grounding in statistical analysis and experimental design for measuring model performance and improvements - Familiarity with evaluation methods for large language models - Comfort working through ambiguity and shipping iteratively YOU’RE LIKELY SOMEONE WHO - Enjoys ownership and customer-facing problem solving - Thinks entrepreneurially and moves quickly - Balances speed with engineering rigor - Communicates clearly with technical and non-technical users WHY ENGINEERS JOIN - Direct exposure to frontier AI research - Real ownership and visible impact - Technical depth paired with human interaction - Fast feedback loops and high leverage - There are very few roles that combine this level of technical rigor, customer proximity, and research exposure. BENEFITS - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K proximity bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

M

Research Engineer, Real Environments

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE You’ll work with large enterprises to capture their data and transform it into high-fidelity RL environments for capability evaluations and training datasets for frontier labs. We focus on pushing the frontier of world-building, verifier engineering, and more alongside our partners. Your goal will be to automate the process of building evals for real work in the economy. WHAT YOU'LL DO - Ship models for workflow extraction, classification, and grading. - Engineer autonomous task refinement processes which distill data taste into pipelines. - Deliver data to customers and deploy into real engagements. - Help define the future of agentic transformation for enterprises around the world. - Deeply learn about the intricacies of enterprises through building evaluations for all aspects of work. - Build end-to-end environments for labs & enterprises by platformizing sandbox app clones, load real data into the sandboxes, build prompts from real workflows, and write verifiers leveraging enterprise expertise & golden outputs. - Systematize the production of environments to scale throughput while maintaining high-quality worlds and verifiers. WHAT WE'RE LOOKING FOR - Prior experience shipping environments – you’ve contributed to an OSS framework, built environments at previous companies, or worked on agentic evaluations. - Strong full-stack engineering skills – you’ll be responsible for everything from infrastructure to app code to analytics - Bias to action – this team is focused on shipping evals, not just philosophizing about them. - Curiosity – being biased towards understanding and digging deep into model behavior and actually looking at the data. - Sweat the details that make a simulation indistinguishable from the real thing and have systems-level thinking skills that allow you to scale up quality. NICE TO HAVE - Experience with Temporal, Modal, or similar orchestration/compute services - Experience with synthetic data generation for frontier models.Past work auditing and scrutinizing industry-standard evaluations BENEFITS - Semi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

M

Mercor AI Safety Fund Grants

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. Mercor Safety Research Grants $5M One of the biggest challenges the industry faces today is addressing whether frontier AI is safe enough to deploy. A model can pass safety checks and still act differently in production. This is why investing today in safety research, evals and verification is critical. Mercor is committing $5 million to fund safety research. Award size and mix depend on the project. The grant supports: - Researcher hours - API costs - Stipends for event and conference attendance - The time of experts from Mercor's platform This is separate from the Mercor Research Fellowship, which funds benchmark and economics work. You can apply to that HERE https://www.mercor.com/careers/?ashby_jid=e1f6792d-1aae-4c2e-8edb-e2b91343dbb5. What we're looking for We're open to proposals across the full range of safety interests. We're particularly interested in: - Misalignment: deceptive alignment, goal misgeneralization, reward hacking, scheming, and situationally-aware failure modes - Sandbox escape: containment failures, privilege escalation, tool misuse, and agents operating outside their intended scope - Evaluation awareness: models detecting they are being tested and behaving differently under observation than in deployment - Interpretability: understanding what models are actually doing internally, and whether that can be made legible to a human reviewer - Oversight and control: scalable supervision, human-in-the-loop reliability, and what breaks when the system is more capable than its reviewer - Red-teaming methodology: more robust systems for uncovering novel failures If your work doesn't fit neatly into these, apply anyway. Strong proposals outside this list are welcome. Why us - Funding for researcher time, API costs, and event attendance - Where useful to the work: access to Mercor's expert network for human grading, red-teaming, and annotation: lawyers, accountants, engineers, scientists, clinicians - Access to Mercor's internal evaluation infrastructure, subject to review - Introductions to Mercor's network of researchers across frontier labs and academia Who should apply - Researchers at universities and non-profit research organizations, as well as public benefit corporations and small startups that do not sell AI training data. Awards are made to the researcher’s institution. - People with a specific, well-scoped question: the grant is built around your proposal, not a generic research rotation - Applicants based in the United States or the United Kingdom - Background in ML, CS, statistics, or an adjacent field (measurement, psychometrics, HCI, security, social science) - Bonus: experience with agentic evaluation, RL environments, adversarial ML, or systems security We expect grantees to publish. A paper, an open dataset, a public methodology, or a tool the field can use, released under an open license that permits commercial use. Award terms Selected grantees will sign Mercor’s research award agreement. Key terms include: - Grantees keep ownership of their work and grant Mercor a non-exclusive license to use it, including commercially. - Mercor may identify grantees and their institutions as award recipients, including by name and logo. Grantees may use Mercor’s logo with Mercor’s approval. - For benchmark and evaluation projects, Mercor receives early access to the benchmark before public release and the exclusive right to build and host a private held-out test set, at Mercor’s cost. - Grantees agree not to work with specified Mercor competitors on substantially similar benchmarks or evaluations during the award and for 12 months afterward. - Grantees are free to publish their findings. Mercor reviews drafts in advance only to remove its confidential information. - Awards are subject to tax documentation, sanctions screening, and periodic progress reports. How to apply Submit an Expression of Interest. We expect to see a one- or two-page document. It should contain at least a section on your team, background, and research accomplishments; a section on your proposed research project; and a section on the outputs and impact of the project, with directionally correct timelines and resource requirements. Please do not include confidential or proprietary information in your Expression of Interest. Submitting an Expression of Interest does not obligate Mercor to make an award.

M

Engineering Manager, Applied AI

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE We’re hiring Engineering Managers to lead teams within our Applied AI organization. Applied AI builds systems that directly improve model quality, including evaluation infrastructure, annotation products, and emerging multimodal capabilities. You’ll lead a team of engineers, partner with product and research, and help define how Mercor stays at the frontier of AI. You will also build partnerships with leading AI labs and directly contribute to improving the quality of frontier models through data, evaluation, and systems. This role requires high ownership and abstraction: setting direction, driving outcomes, and staying hands-on while leading. WHAT YOU’LL WORK ON BUILD AND SCALE TEAMS - Manage and grow a team of 6–10 engineers - Coach and develop high-potential engineers - Establish strong ownership, culture, and execution standards - Shape the team: define hiring processes, establish engineering practices, and scale a high-performing team from the ground up DRIVE APPLIED AI SYSTEMS AND OUTCOMES - Drive insights and methodology behind evaluation systems that benchmark and improve model performance - Lead development while driving improvements in data quality, operational efficiency, system stability, and scalability - Scale products that generate high-quality training data and improve human-in-the-loop workflows OWN TECHNICAL DIRECTION - Lead system design for complex AI systems - Stay close to the technical work and guide engineers through ambiguous problems - Translate high-level AI goals into clear engineering roadmaps and execution plans OPERATE ACROSS A BROAD SCOPE - Partner with product, research, operations, and external AI labs - Work across systems, data insights, and custom partnerships to drive model quality SHAPE THE ORGANIZATION - Introduce lightweight processes as the organization scales - Hire and develop strong engineering talent - Help define engineering management at Mercor WHAT WE’RE LOOKING FOR - Bachelor’s Degree in Computer Science or a related technical degree such as Electrical Engineering, Physics, Mathematics or Statistics - 6–10 years in engineering; 2–3+ years managing teams - Strong background in building scalable systems - Ability to lead in ambiguous environments with sound technical judgment - Proven ability to coach engineers and drive execution - Strong ownership mindset with pragmatic decision-making Who Thrives Here - We’re looking for engineering leaders who take ownership in ambiguous environments, make pragmatic decisions, and consistently deliver measurable impact. SUCCESS METRICS - Build high-performing, well-supported teams - Hire and retain strong engineers - Improve team velocity and execution quality - Contribute to systems that measurably improve model performance LOCATION - San Francisco (preferred) or New York - Relocation assistance will be provided as needed WHY THIS ROLE - Direct impact on frontier AI development - Build partnerships with leading AI labs and shape how frontier models improve - High ownership across team, systems, and hiring - Opportunity to shape a rapidly scaling organization - Work at the intersection of engineering, product, data, and AI Benefits - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

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Data Scientist

Negotiable

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. WHAT YOU’LL DO In your first year you’ll ship analyses and experiments that move core product metrics, match quality, time-to-hire, candidate experience, and revenue. You’ll: - Define north-star and feature-level metrics for our ranking, interview analytics, and payouts systems. - Design/run A/B tests and quasi-experiments; turn results into product decisions the same week. - Build source-of-truth dashboards and lightweight data models so teams can self-serve answers. - Instrument events with engineers; improve data quality and latency from ingestion to insight. - Prototype quick models (from baselines to gradient boosting) to improve matching and scoring. - Help evaluate LLM-powered agents: design rubrics, human-in-the-loop studies, and guardrail canaries. YOU’LL THRIVE HERE IF You have solid fundamentals (statistics, SQL, Python) and projects you’re proud to demo. You iterate fast, frame the question, test, and ship in day, and care as much about clarity of communication as you do about p-values. Curiosity about LLM evaluation, retrieval, and ranking is a bonus; you’ll learn alongside folks who’ve shipped at Jane Street, Citadel, Databricks, and Stripe. QUALIFICATIONS - 0–10 years in data science/analytics or similar; BS/BA in a quantitative field (or equivalent work). - Strong SQL; Python for analysis; comfort with experiment design and causal thinking. - Communicates crisply with engineers, PMs, and leadership; turns analysis into action. - Nice-to-haves: dbt, dashboarding (Hex/Mode/Looker), marketplace or search/recommendation metrics, LLM/agent evaluation. BENEFITS - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

M

Principal Solutions Architect

Negotiable

COMPANY OVERVIEW: Maven AGI is an enterprise https://www.mavenagi.com/ AI platform on a mission to unleash business artificial general intelligence (AGI), starting with customer service. Founded in July 2023 by executives from HubSpot, Google and Stripe, Maven builds conversational AI agents capable of delivering accurate, autonomous support that delights customers at scale. Our platform unifies fragmented systems, integrates knowledge and personalization sources, and enables intelligent actions - all without costly system changes. We’re laying the foundation for a future where our technology handles complex tasks, allowing people to focus on what they do best: creative problem-solving, relationship building, and delivering exceptional customer experiences. We’ve started by reimagining the enterprise customer experience with a support use case. We believe that today’s support experience is broken: slow and painful for customers, and expensive and human capital intensive for companies. We are building Maven to deliver better, cheaper support, for both end users and agents. With recent advancements in Generative AI, it is now possible to deliver delightful customer experiences at a fraction of today’s cost. ROLE SUMMARY Maven is scaling from bespoke AI agent deployments to repeatable enterprise implementations. Our largest customers need someone who can de-risk complex technical architectures before signature, guide production launches across messy enterprise systems, and turn each deployment into reusable leverage for the next one. As a Principal Solutions Architect, you will be the technical design authority for Maven’s most strategic enterprise accounts. You will own pre-SOW technical validation, design production-grade AI agent architectures, guide Forward Deployed Engineers from POC through launch, and help convert field learnings into reusable platform capabilities. This is a forward-deployed, customer-facing technical leadership role. You should be comfortable prototyping quickly, validating quality with data, explaining complex AI systems to executives, and going deep with staff engineers on integrations, data flows, evals, observability, privacy, and fallback behavior. WHAT YOU’LL OWN Pre-SOW technical validation Partner with Sales, Engagement, and Solutions Engineering to validate platform fit, integration complexity, data/security constraints, FDE effort, and implementation risks before strategic SOWs are finalized. Production architecture for strategic accounts Design reference architectures for multi-channel AI agent systems across voice, chat, email, and SMS, including integrations with CCaaS, CRM, data warehouse, identity, and internal knowledge systems. Define data contracts, tool/action patterns, observability, privacy boundaries, fallback behavior, and acceptance gates. Technical direction from POC to launch Guide FDEs through architecture decisions, edge cases, CI/CD, evals, monitoring, rollback plans, and launch readiness. You own technical direction and architecture quality; FDEs own implementation. Reusable deployment leverage Turn each strategic deployment into reusable assets: reference architectures, integration templates, eval harnesses, implementation checklists, demo baselines, or product requirements that reduce effort for future launches. Executive and technical customer communication Lead architecture reviews, security reviews, roadmap sessions, and technical deep dives with VP/C-level sponsors, security teams, and staff engineers. Make trade-offs clear for both executive and deeply technical audiences. Field-to-product feedback loop Identify repeated customer needs and implementation gaps. Partner with Product and Engineering to turn field patterns into roadmap inputs, reusable platform capabilities, and core product improvements. WHAT SUCCESS LOOKS LIKE Within your first 6–12 months: - Strategic deals have clearer technical feasibility, integration scope, risk, and effort estimates before SOW. - FDE teams move faster because they have stronger architecture guidance, launch gates, and reusable implementation assets. - Complex deployments launch with better observability, evals, fallbacks, and privacy controls. - Customers trust Maven’s technical direction in security, architecture, and executive-level reviews. - Repeated deployment patterns become productized rather than remaining bespoke. WHAT MAKES YOU A FIT REQUIRED - 7+ years in Solutions Architecture, Applied Engineering, Forward Deployed Engineering, Sales Engineering, or equivalent customer-facing technical leadership for enterprise SaaS, data, ML, or AI products. - Track record taking complex enterprise technical solutions from discovery or POC into production. - Strong system design skills across APIs, event-driven systems, data modeling, identity/consent flows, latency, throughput, reliability, and observability. - Practical AI/LLM depth: RAG, tool use/function calling, prompt and retrieval evaluation, safety guardrails, agent orchestration, and quality measurement. - Hands-on technical fluency in TypeScript/Node, Python, or similar. You are not the primary production engineer, but you can prototype, review code, debug integrations, and reason through implementation trade-offs. - Excellent written, verbal, and visual communication. You can lead a security review, produce a clear architecture diagram, and present trade-offs to executives. - Builder mindset. You want to help create the Solutions Architecture function, not just operate inside an existing one. NICE TO HAVE - Contact center / CCaaS experience with platforms like Genesys, Five9, NICE, Twilio, or Amazon Connect. - Depth with Zendesk, Salesforce Service Cloud, ServiceNow, or similar customer support systems. - Experience in regulated or complex data environments such as financial services, healthcare, gaming, marketplaces, or franchise businesses. - Experience at a frontier AI company, FDE-style company, data/ML platform company, high-growth SaaS company, or as a technical lead on complex enterprise AI programs. - Prior experience building a technical function from zero to one.

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Software Engineer, AI Tooling

Negotiable

WHAT MATX IS BUILDING MatX's mission is to make the world’s best AI models run as efficiently as allowed by physics, bringing the world years ahead in AI quality and availability. We build custom silicon for training and running the world's largest AI models, with hardware/software co-design across ISA, RTL, simulator, compiler, and kernels. The AI tooling team sits across that whole stack and owns the agent harnesses and shared workflows that turn general-purpose coding agents into ones maximally useful on each layer. ABOUT THE POSITION Frontier agents are stronger on our Rust and Python than on our Bluespec and SystemVerilog. What context, tools, and checks make an agent trustworthy on a testbench or a timing report, and how do you know? Very few people anywhere have worked through these questions on a chip that has to tape out, with the engineers who sign off on it working next to them. You'd work side-by-side with our hardware engineers, learn how RTL design, design verification (DV), and physical design (PD) work here, codify what's already working, and build what's missing. WHAT YOU'LL DO HERE: - Close the gap where agents are worst at hardware engineering work. Pair with RTL, DV, and PD engineers while they work, watch where their agents stumble, and identify and ship context, tools, or harness improvements. - Build tools to address problems that only exist now that agents write code, e.g. a review tool that breaks a large agent-generated diff into reviewable pieces. - Build the eval loop for our AI tooling by taking bugs out of our repo's history, replaying them against different agent configurations, and using the results to decide what we adopt. WHO YOU ARE: - Have RTL design, DV, or PD fluency, or will build it fast. You don't need to have taped out a chip, but you'd be working RTL, DV, and PD problems daily and judging to what extent an agent’s answer is accurate. - Use coding agents heavily and have strong opinions on what you let them do, what you don't, and how you verify their work. - Built engineering workflows (methodology, flow automation, review tooling, agent harnesses) that other engineers adopted and kept. - Notice LLM agents asserting something confidently wrong and modify their harness in response. BONUS POINTS IF YOU HAVE: - Built tooling or automation for chip design flows. - Are familiar with Bluespec or another high-level HDL, or with architecture and performance simulators. COMPENSATION The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation. - Early Career - $160,000 - $275,000 + equity - Mid Career - $175,000 - $450,000 + equity - Senior Career - $275,000 - $600,000 + equity What We Offer - Time off: 4 weeks PTO (accrued) + 12 company Holidays + up to 3 weeks remote work - Health: Company-subsidized Medical (Kaiser or Anthem) for employees & dependents, Guardian Dental and Vision insurances for employee & dependents, and life insurance (employee only), plus HSA and FSA offerings via Lively. - Financial Security: Choose from Roth or pre-tax 401K (or both) with a 5% company contribution (even if you don't contribute), 100% company-paid life insurance (up to $300K), long-term disability and travel insurance. - Professional Development: $1500 per year Professional Development Budget - Team Meals: MatX provides onsite team lunch & dinner Monday - Friday, with your choice of ordering via WeBox, Specialty’s or via our reimbursement system - Commute on Us: We pay 100% for your daily commute. Commute via our company rideshare account, reimburse public transportation, or get a Fasttrak card (or all three) - MatX E[x]tras: $50/mo to use on the perk you value most - Cell & Internet Reimbursement: $35/mo for cellular and $40/mo for wifi - Remote Home Setup: Use our Amazon business account for up to $1000 in office setup purchases. - Mental Wellbeing: 100% paid mental health benefit via SpringHealth and Guardian EAP. - Support to Parents: Up to 12 weeks paid parental leave regardless of path to parenthood, 10 weeks pregnancy disability leave, flexible return-to-work hours, and a $6K Family Fund to cover fertility, surrogacy, adoption, menopausal health, preservation, family counseling and more. As part of our dedication to the diversity of our team and our focus on creating an inviting and inclusive work experience, MatX is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, gender, gender identity, gender expression, sexual orientation, age, physical or mental disability, medical condition, marital/domestic partner status, military and veteran status, genetic information or any other legally recognized protected basis under federal, state or local laws, regulations or ordinances. --------- This position requires access to information that is subject to U.S. export controls. This offer of employment is contingent upon the applicant's capacity to perform job functions in compliance with U.S. export control laws without obtaining a license from U.S. export control authorities. -------- MatX does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or People team and any resumes submitted are deemed to be the property of MatX.

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Runtime Engineer

Negotiable

WHAT MATX IS BUILDING MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together. WHAT YOU'LL DO HERE - Build the host-side interface library — device memory management, DMA, streams and events, sync primitives — that every compiler-emitted program runs on top of - Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently - Design the custom-kernel ABI — calling convention, sync semantics, lifecycle — and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device - Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers - Build the LLM inference serving stack — paged KV cache, continuous batching, request scheduling, token streaming — and the cluster orchestration primitives underneath it - Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks - Design what the chip exposes to host-side profilers and debuggers — perf counters, traces, and the Python surfaces ML engineers actually use — and hit measurable performance targets on runtime overhead and serving throughput WHO YOU ARE - Strong experience in a systems programming language — Rust, C, C++, or Go — including memory management, allocator design, and FFI/ABI work - Have built Python interop layers in production (PyO3, ctypes, pybind11, or equivalent C-ABI bridging) - Have designed and maintained API or ABI contracts between teams — versioning, evolution, breaking-change discipline — not just consumed someone else's - Hands-on with at least one accelerator programming model (CUDA, ROCm, oneAPI Level Zero, TPU, or comparable) — enough to reason about device memory, async execution, and kernel launch - ML-systems literate — comfortable with the training and inference loop, what collectives do, what a tensor layout is. Research depth not required. BONUS POINTS IF YOU HAVE - LLM inference internals — vLLM, TensorRT-LLM, or SGLang (paged attention, scheduler design) - Rust at depth, including proc macros, unsafe with soundness reasoning, and complex lifetime/trait work - Custom allocator design (slab, paged, arena) or other low-level memory work - ML framework integration experience (PyTorch custom backends, JAX/XLA, ONNX runtime) - Profiler or tracing infrastructure work (perfetto, Nsight, or a custom stack) - Driver-adjacent or kernel-bypass work, or prior new-silicon bring-up COMPENSATION The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job-related skills, and relevant education and training. Career length is only a guideline for compensation. - Early Career - $160,000 - $250,000 + equity - Mid Career - $175,000 - $362,500 + equity - Senior Career - $250,000 - $475,000 + equity What We Offer - Time off: 4 weeks PTO (accrued) + 12 company Holidays + up to 3 weeks remote work - Health: Company-subsidized Medical (Kaiser or Anthem) for employees & dependents, Guardian Dental and Vision insurances for employee & dependents, and life insurance (employee only), plus HSA and FSA offerings via Lively. - Financial Security: Choose from Roth or pre-tax 401K (or both) with a 5% company contribution (even if you don't contribute), 100% company-paid life insurance (up to $300K), long-term disability and travel insurance. - Professional Development: $1500 per year Professional Development Budget - Team Meals: MatX provides onsite team lunch & dinner Monday - Friday, with your choice of ordering via WeBox, Specialty’s or via our reimbursement system - Commute on Us: We pay 100% for your daily commute. Commute via our company rideshare account, reimburse public transportation, or get a Fasttrak card (or all three) - MatX E[x]tras: $50/mo to use on the perk you value most - Cell & Internet Reimbursement: $35/mo for cellular and $40/mo for wifi - Remote Home Setup: Use our Amazon business account for up to $1000 in office setup purchases. - Mental Wellbeing: 100% paid mental health benefit via SpringHealth and Guardian EAP. - Support to Parents: Up to 12 weeks paid parental leave regardless of path to parenthood, 10 weeks pregnancy disability leave, flexible return-to-work hours, and a $6K Family Fund to cover fertility, surrogacy, adoption, menopausal health, preservation, family counseling and more. As part of our dedication to the diversity of our team and our focus on creating an inviting and inclusive work experience, MatX is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, gender, gender identity, gender expression, sexual orientation, age, physical or mental disability, medical condition, marital/domestic partner status, military and veteran status, genetic information or any other legally recognized protected basis under federal, state or local laws, regulations or ordinances. --------- This position requires access to information that is subject to U.S. export controls. This offer of employment is contingent upon the applicant's capacity to perform job functions in compliance with U.S. export control laws without obtaining a license from U.S. export control authorities. -------- MatX does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or People team and any resumes submitted are deemed to be the property of MatX.