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About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Building a great AI assistant is only half the battle – knowing whether it's actually great is the other half. Our team owns the measurement and quality layer that make Glean's Assistant and Agents reliably better over time: evaluation pipelines, quality evalsets, LLM-powered judges, agent observability, and the tooling engineers use to understand what changed and why. It's a rare combination of infrastructure engineering, applied ML, and direct product impact. If you care deeply about quality and want to build the systems that make it measurable, this role is for you. You will: Design and curate evaluation datasets – sampling strategies, query diversity, and golden sets that give reliable, representative coverage of real assistant behavior. Build and maintain large-scale evaluation pipelines that measure assistant quality across thousands of real user queries. Build LLM-powered judges that score metrics like correctness, completeness, and response quality, and align them against human judgment. Evaluate new models and product changes before they ship – providing the quality signal that gates launches and prevents regressions. Build observability infrastructure for AI agents: trace enrichment, data pipelines, and dashboards that make assistant behavior inspectable. Close the loop between quality measurement and improvement using eval results, customer feedback, and techniques like automated prompt iteration to help drive concrete gains in assistant behavior. Collaborate with engineers across the company to make evals a first-class part of how we ship. About you: 2+ years of software engineering experience with strong coding skills. Strong backend fundamentals in Go and Python; comfortable with distributed data pipelines. Experience working with LLM evaluation, reinforcement learning from human feedback, natural language processing, or other large systems involving machine learning. Analytically rigorous – you think carefully about what offline metrics actually predict about real user experience. Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company You care about quality – not just in the systems you build, but in the product you're helping measure and improve. Location: This role is hybrid (3-4 days a week in one of our SF Bay Area offices) Compensation & Benefits: The standard base salary range for this position is $200,000 - $300,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is seeking a few Machine Learning engineers who want to focus on a combination of Quality and traditional ML work to help us build the Enterprise Brain. The Enterprise Brain team is developing a suite of proactive AI products that aims to revolutionize enterprise workflows by proactively detecting and automating tasks for users - thus unlocking true productivity. This is built on top of a deep user understanding and state of the art Enterprise graph. The project involves using both LLM and other advanced ML techniques, agent orchestration and cutting-edge ranking techniques. You will: Work on deeply challenging ML problems involving user understanding and task prediction. Invent new LLM workflows and signals to improve reasoning, planning, and personalization. Design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction and other agentic systems. Lead development of scalable evaluation, benchmarking, and optimization loops. Build and maintain robust ML pipelines for enterprise and knowledge graph construction. Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance. Collaborate with cross-functional teams to deeply understand customer pain points and deliver high-quality, production-ready ML solutions. Mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment. About you: 3+ years of industry experience in AI or Machine Learning Engineering. BA/BS in computer science, math, sciences, or a related field. Experience with search, recommendation, natural language processing, or other large-scale ML systems. Proven ability to design, build, and ship production-ready models and systems. Demonstrated expertise in ML evaluation, benchmarking, and data quality—ideally with experience in building or maintaining evaluation frameworks for complex enterprise tasks. Proficiency in your ML framework of choice (e.g., TensorFlow, PyTorch). Strong coding skills (Python, Go, Java, C++, etc.). Thrive in a customer-focused, cross-functional environment; a proactive and positive attitude is a must. Location: This role is hybrid (4 days a week in our Mountain View, CA office) Compensation & Benefits: The standard base salary range for this position is $200,000 - $300,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.

C

Staff Machine Learning Engineer

Negotiable

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce. We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day. Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world. Role Overview As our Staff Machine Learning Engineer for Coupang Media Group, Ads Quality, you will be responsible for developing, designing machine learning models, optimization algorithms and new product features for our advertising platform. The Coupang Media Group (CMG) is responsible for providing vendors that sell their products on Coupang a host of marketing products and services.  We launched Product Ads performance advertising marketplace late in 2018.  We have grown rapidly to over 10,000 active advertisers and 150M daily ad impressions in only 5 months’ time. Our group is responsible for advertising brands and products inside our e-commerce website. To get an idea of how valuable that is, both Alibaba and Amazon make more money on advertising than on selling products. This is an opportunity to be part of building up a new development organization with strong revenue potential inside a successful company. We’re building full-stack engineering teams to handle large scale advertising problems, including real-time behavioral targeting, auctioning and bidding systems and search based advertising. We are in the growth phase of the organization, so you’ll be a part of managing the transition from growth phase to enterprise processes. In this role you will be responsible for innovating and building our new ad quality stack for our ads platform  and eventually extend it into a full ad exchange, DSP, and SSP. We currently handle about 1bn impressions per day from internal traffic alone. What You Will do Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence Define and track key performance metrics to evaluate model impact and identify high[1]leverage opportunities for improvement Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning Basic Qualifications Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields 4 years of professional experience in applied machine learning Experience in machine learning, deep learning, and statistical modeling Proficiency in Python and/or Java, with experience in building production grade ML systems Preferred Qualifications Master’s or PhD in relevant technical fields Experience with search systems, information retrieval or recommendation engines Experience with LLMs, embeddings, and vector search technologies Experience with cloud platforms such as AWS, Google Cloud Platform including services like Vertex AI, BigQuery or SageMaker Experience working in startup or high-growth environments Proven ability to lead-cross-functional teams and deliver results in a multicultural, global organization Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, Scikit[1]learn, Keras, XGBoost, LightGMB, and H2o.ai Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders Demonstrated ability to work independently and manage ambiguity in fast-paced environments Pay & Benefits Our compensation reflects the cost of living across several US geographic markets. At Coupang, your base pay is one part of your total compensation. The base pay for Staff position ranges from $152K/year to $261K/year. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience. General Description of All Benefits Annual bonus is 0-20% of the base salary Medical/Dental/Vision/Life, AD&D insurance Flexible Spending Accounts (FSA) & Health Savings Account (HSA) Long-term/Short-term Disability Employee Assistance Program (EAP) program 401K Plan with Company Match 18-21 days of the Paid Time Off (PTO) a year based on the tenure 12 Public Holidays 6 weeks Paid Parental leave Pre-tax commuter benefits MTV - [Free] Electric Car Charging Station General Description of Other Compensation “Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale. Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate. Recruitment Process and Others Recruitment Process Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances. Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage. Details to Consider This job posting may be closed prior to the stated end date for application if all openings are filled. Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process. Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws. Privacy Notice Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/ If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com . Requisition ID: R0072711

About Hightouch Hightouch is an Agentic Marketing Platform powered by the industry-leading Composable CDP. With complete brand context, customer data, and performance history in one place, every marketer finally has the power to build and ship end-to-end campaigns themselves. Teams move faster, stay on brand, and get AI marketing that actually works. Founded in 2019 and headquartered in San Francisco, Hightouch enables marketing teams to analyze performance, brainstorm ideas, and generate creative at a speed and quality that wasn't previously possible. Named a Leader in the 2026 Gartner® Magic Quadrant™ for Customer Data Platforms, Hightouch is trusted by leading enterprises like Domino's, Spotify, Aritzia, Cars.com, Ramp, and PetSmart. At Hightouch, our mission is to help our customers leverage data and AI to grow their businesses. The team is ambitious, impact-driven, efficient — and we believe humility, kindness, and compassion are essential to our success. If you're energized by velocity, obsessed with raising the bar, and want to build alongside people who care deeply about each other and our customers, we'd love to meet you. About the Role We are looking for an engineering leader to lead machine learning efforts across Hightouch. While hundreds of companies use Hightouch today to sync data into their SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figuring out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. And given our access to data warehouses and databases, Hightouch is perfectly placed to make use of a company’s customer data in building a powerful intelligence layer. At Hightouch, our engineering leaders serve as both active technical voices and strong people managers. The key outcomes we are looking for from this role include: Product Development: Lead the team in roadmapping and executing successful development of significant features and products as required to drive the business forward. Uphold a high technical bar, while making pragmatic tradeoffs Pace: Through a combination of leadership and technical expertise, improve the pace of execution of the team Recruiting: Be responsible for growing the team, including identifying needs, deciding how to evaluate candidates, and ensuring we hire high impact team members Reliability: Against the uphill battle of high growth/scale, up-level the team on reliability, resulting in fewer incidents, catching our own issues before our customers do, and a strong incident management hygiene People: Foster growth of team members and ensure strong engagement and motivation Some of the problems we’ll be working on include: Personalization and Product Recommendation : There are often many options for what content a company could message a user with, including which products to show from catalogues. Given this large state space, how can Hightouch help personalize messages with the most relevant content for each user? Probabilistic Identity Resolution: Many of our customers have multiple datasets and sources of information on their users. How do we stitch these datasets together and decide which profiles are from the same user or household? Content Generation : Particularly with recent advances in LLMs, how can we help marketers generate text, images, and creatives that are compelling to their customers? ML Infrastructure: Building the infrastructure to support model training and inference at scale for some of the largest companies in the world. What We Offer We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $230,000 - $400,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window. We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for. E-Verify Statement Hightouch participates in E-Verify. After you join the team, we'll verify your eligibility to work in the U.S. by submitting information from your Form I-9 to the Social Security Administration and, if needed, the Department of Homeland Security. This process happens post-hire only — we never use E-Verify to pre-screen applicants. E-Verify Notice E-Verify Notice (Spanish) Right to Work Notice Right to Work Notice (Spanish)

About Lightfield Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context: accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. We’re rethinking CRM from first principles. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth. We’re building the CRM platform we always wished existed: fast, intelligent, and genuinely helpful. We are backed by Greylock, Lightspeed, and Coatue, and our founders previously built Tome, a generative AI presentation product used by over 25 million people. Before Lightfield, our team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce. About the role Lightfield's AI/ML team builds the experiences at the core of our product, developing new applications to wow our customers. Today, the team is focused on building a powerful, domain-specific AI that outperforms generic LLMs We’re inspired by the challenge of creating innovative new AI products for people doing serious work, and we’re looking to grow our AI/ML team to meet that challenge. What you'll do Lead the development of ML product development infrastructure, focusing on scaling and innovating in areas of collaboration and versioning, particularly in the context of LLM model training and prompting Create and maintain a platform that will be used by multiple teams working on ML products, ensuring its scalability, efficiency, and user-friendliness. Collaborate closely with internal teams to integrate ML solutions and define best practices for software engineering in an AI-driven development landscape. Help build a world-class AI/ML engineering team by recruiting and mentoring teammates Address and solve open-ended technological challenges in software engineering at scale, especially in the context of AI-driven systems. Who You Are You have a BS or MS degree in CS, Engineering, AI or a related field. 6+ years experience in software engineering with a focus on ML infrastructure. You have a strong understanding of deep learning AI/ML frameworks or cloud services Experience with the integration of software engineering with large language models. Ability to navigate and solve open-ended technological challenges in a fast-evolving AI landscape. Excellent collaboration skills, with the ability to work effectively with both internal teams and external partners. Strong problem-solving skills and the ability to handle complex, cross-functional projects. Bonus Points Publications in applied AI/ML scientific journals Experience navigating open source/vendor solutions in LLM ops space (LangChain, Llama, Pinecone, etc) Benefits & Perks Competitive salary Meaningful early equity Health insurance (medical, dental, vision) 3 weeks of PTO 11 paid company holidays + we enjoy a winter holiday break 3 months of paid family leave Wednesdays work from home Regular team dinners, events, offsites, and retreats 401k plan Other perks include: commuter and lunch stipend

About Bree Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck. We operate in a massive market that’s historically been underserved by traditional financial institutions, and we’re building products that help customers access short-term credit with a transparent, user-first experience. To date, 800,000+ Canadians have signed up for Bree—and we believe we’re still early. We’re at an exciting intersection of product-market fit, rapid growth, and a clear path to becoming one of the most important fintech companies in Canada. We’re at 8-figures of annualized revenue, growing quickly, and profitable. We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after. About the Role We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology. What You'll Do Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. Apply machine learning design patterns to build modular, reusable, and production-ready models. Collaborate with data engineers to develop high-performance data pipelines for training and inference. Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques. What You'll Need Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch. Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques. Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows. Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL). Knowledge of cloud-based ML deployment and infrastructure management. Ability to implement real-time and batch inference pipelines efficiently. Strong analytical and problem-solving skills to translate business needs into scalable ML solutions. Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy. Benefits: 💰Top of the market compensation for top performers ⚕️Comprehensive health, dental, and vision benefits plan 🖥 $1,500 annual learning & home-office stipend 🧘🏼 $1,000 annual wellness stipend 🍔 Monthly Lunch Stipend 🚗 Commuter Benefits 🚼Paid Parental leave 🏝20 annual PTO days + unlimited sick days 🚀 Quarterly Team Gatherings ☕ In Office Amenities

About Us Relace is building the models and infrastructure that code agents reach for. We power the fastest model on OpenRouter (10,000 tok/s) and deliver optimized small language models designed for retrieval, application, and core code generation functions. Our technology supports some of the world’s fastest-moving companies — including Lovable, Figma, and Vercel — as they deploy and scale code generation to hundreds of millions of users. We recently raised our Series A from a16z, and we’re growing quickly. Our team is made up of mathematicians, physicists, and computer scientists who are deeply passionate about their craft. If you thrive on ambitious technical problems, care about elegant systems design, and want to build the foundation of how code gets written at scale, this is the place for you. The Role We’re looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable through low-level optimizations and smart systems design. The ideal candidate is excited by CUDA kernels, memory layouts, GPU scheduling, and squeezing performance out of complex training and inference workloads. They should be just as comfortable optimizing compute and networking paths as they are working alongside research teams to productionize new architectures. This is a role for someone who enjoys deep performance tuning, understands the realities of running large-scale ML systems, and thrives in fast-moving, high-leverage environments. Requirements Strong background in systems-level ML engineering. Experience with CUDA, GPU kernel optimization, and performance tuning. Fluency in Python and at least one systems language (C++ or Rust preferred). Familiarity with distributed training frameworks (e.g., PyTorch, JAX, DeepSpeed, or similar). Experience working with large-scale training or inference infrastructure. Understanding of memory management, parallelization, and hardware-aware model optimization. 2+ years of experience working in ML infrastructure or performance-critical environments. Willingness to work in-person from our SF office in FiDi.

About Sandbar Sandbar is an interface company in New York City. We aim to augment individuals so we can each think, act, and move more freely. Our team has built SW, ML, and HW products across Meta, CTRL-labs, Google, Apple, Fitbit, Peloton, and Equinox. Our first product, Stream, is a self extension—a private voice ring and conversational interface. Stream has been featured in WSJ, Bloomberg, & Wired, and begins shipping in Summer '26. Join us in creating technology that extends human thinking. About We’re looking for a machine learning engineer to help build Stream, a new conversational computer. As a machine learning engineer, you will develop a system which spans voice, memory, and agentic control. This role is perfect for someone cares deeply about real-world ML deployment and human-in-the-loop agentic interactions. Responsibilities Develop, evaluate, and deploy audio models spanning cloud models to resource-constrained on-device settings Optimize inference pipelines for latency, reliability, and concurrency Work closely with other ML/AI engineers, infrastructure engineers, designers, and cofounders on existing and future products Qualifications 4+ years in machine learning experience Experience shipping ML-based products is required Experience developing audio / voice models is required Passion for human-computer interaction FTE Benefits Health, vision, and dental benefits Company-sponsored 401(k) Unlimited PTO and sick time Early stage equity

Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. Location: Remote - United States or Canada From Home / Beach / Mountain / Cafe / Anywhere! We are a remote-first company with a globally distributed team. You can find your productive zone and work from there. About The Role As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale. This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges. What you’ll be doing: Build and optimize data pipelines and backend services to process device and behavioral data in real time. Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production. Turn raw data into production-ready features that feed our fraud detection systems. Collaborate with platform and backend engineers to integrate models seamlessly. Maintain high standards of security, privacy, and compliance. Champion best practices in testing, documentation, and observability. What you’ll need: 5+ years in software engineering, with strong backend experience (Go or Python). Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.). Strong SQL skills and familiarity with relational and non-relational databases. Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration. Excellent communication skills in English, both written and verbal. Bachelor's or Master's in Computer Science, Engineering, or a related discipline. Bonus Points Domain knowledge in fraud, risk, or cybersecurity. Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework. Understanding of modern browser APIs and high-entropy data collection techniques. Familiarity with leveraging frontier LLMs for automation. Benefits we offer: Generous compensation in cash and equity Early exercise for all options, including pre-vested Work from anywhere: Remote-first Culture Flexible paid time off and Year-end break Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific 4% matching in 401k / RRSP - US and Canada specific MacBook Pro delivered to your door One-time stipend to set up a home office — desk, chair, screen, etc. Monthly meal stipend Monthly social meet-up stipend Annual health and wellness stipend Annual Learning stipend Join a fast-growing company with world-class professionals from around the world. If you are seeking a meaningful career, you found the right place, and we would love to hear from you. To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice .

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. The Role Two Dots is hiring a Machine Learning Engineer for a low-headcount, high-impact role focused on technically difficult applied ML problems in housing verification, underwriting, fraud detection, and document understanding. This is not a research role, although the right person has the depth to develop models from scratch end-to-end. Some of the problems we are facing are genuinely hard: detecting whether a PDF was forged or edited, inferring latent financial profiles from messy payment data, extracting information from noisy documents with very high reliability, and solving chatbot or agent quality problems that big foundation models do not solve out of the box. They should be math literate, comfortable with PyTorch, evaluation, model deployment, quality management, metrics-driven evaluation, and data warehouse-oriented SQL such as BigQuery. What You'll Work On Document forensics and detecting fraudulent or edited PDFs Cash flow underwriting: inferring a latent financial profile from paystubs, bank statements, business data, or other payment data Extracting information from unstructured or noisy sources with very high reliability Solving chatbot and agent quality problems that are too hard for others to solve Developing models, evaluation systems, and quality management processes from scratch Creating broad-based, systemic improvements in ML, LLM, and agent performance Educating the team on how to evaluate ML pipelines and workflows, including workflows that involve prompting foundation models The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad who was a top 2,000 LoL player (he is no longer playing the game, thank god), and a former agave farmer who started a shipping and logistics company while at Stanford. What We're Looking For You should be able to take an ambiguous problem, like PDF fraud detection, and turn it into a reasonable technical plan without needing a well-defined box. You should understand the company strategy well enough to know what is more and less likely to be valuable in ML without escalating every decision or planning process to the most senior levels of management. You should have a strong command of: Tensors, PyTorch, training loops, and model deployment Metrics-driven evaluation and rigorous quality management Statistics, regularization, overfitting, training schedules, and GPU memory management Computer vision, NLP, and multimodal understanding problems Data warehouse-oriented SQL, especially BigQuery Explore-vs-exploit tradeoffs in applied ML work You should be interested in the company mission through a technical lens: consumer underwriting, document understanding, fraud detection, multimodal understanding, and systems that reveal rather than conceal the real affordability crisis in housing. Despite the more cerebral nature of the role, this is an applied and impact-focused position. The work requires patience with exploration, but also the judgment to know when a good-enough solution under time pressure is better than searching for a global optimum. About the Interviews ML phone screen If you do not know how PyTorch, training, and evaluation work, and cannot talk about real modeling work you have done, we will filter you out at this stage. Behavioral interview We will assess whether you are actually interested in working at a startup, whether you can deal with ambiguity, and whether you are more of a pure researcher than an applied builder. ML foundations interview We will test rigorous knowledge of math, statistics, ML foundations, metrics and evaluation, tensors, regularization, overfitting, training schedules, and GPU memory management. Ambiguous problem design We will ask you to convert a hard, ambiguous problem into a reasonable plan. Explore-vs-exploit judgment We will construct a scenario where you need to choose a good-enough solution under time pressure instead of searching for a global optimum.

Company Overview: Root Access is an applied AI company building developer tools. We help mission-critical hardware teams leverage purpose-built AI to program and certify their systems faster. Role Description: The Machine Learning Engineer will be responsible for designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and improving the product. They work with data to create models, perform statistical analysis, and train and retrain systems to optimize performance. Their goal is to build efficient self-learning applications that will delight customers. This is an early-stage company with ambitious goals. You might be a good fit if you have: Proficiency in PyTorch and modern-transformer based systems Experience with AWS for scalable ML service deployment Experience building with Agentic AI frameworks (e.g., RAG, Langchain, MCP, etc) Have 1-3+ years of full-time experience in an MLE role What We’re Looking For: Strong ML Foundations - Experience with recommender systems, embeddings, foundation models. You understand when to use the fancy stuff—and when to keep it simple. Production Mindset - You’ve shipped ML systems that run in the real world. You write reliable Python, know your way around infra basics, and care about performance. Data Agility - You’ve worked with messy data—scraping, parsing, cleaning, and transforming it into something your models can learn from. Frontend Awareness - You’re not expected to be a frontend engineer, but you know how to make ML feel native in a modern React-based product. High Ownership DNA - You see the problem, spec the solution, and ship. You don’t need permission—you need a challenge. 1-of-1 Energy - You’ve been underestimated, or boxed in. You're ready to work somewhere that lets you fully show what you're capable of.

About Us: webAI is the first end-to-end private AI platform. Enterprises and Governments use webAI to bring AI to their data, powering specialized intelligence trained on their own knowledge and compounding in value with every use. Our approach is guided by a simple philosophy: AI should be specialized, sovereign, efficient, and sustainable. It should solve real problems, not just burn more compute. Our technology enables the development of powerful AI using limited amounts of data, challenging the underlying assumption that big data is the key to unlocking the full power of AI. We are offering a superior career opportunity to join a dynamic and fast-growing team that fosters an exciting and growth-oriented work culture; an opportunity where you can truly put your name on meaningful change for an entire civilization. About the Role: We are seeking an experienced Staff Machine Learning Engineer with a strong background in Large Language Models (LLMs) and/or Mixture of Experts (MoEs). The ideal candidate will have a proven track record of developing and deploying advanced AI models. Responsibilities: Lead the development and optimization of Large Language Models and Mixture of Experts models. Collaborate with cross-functional teams to integrate ML models into our platform. Conduct cutting-edge research in machine learning, with a focus on improving the performance and efficiency of LLMs. Stay abreast of the latest advancements in AI and ML, and apply this knowledge to improve our models and methodologies. Mentor junior engineers and contribute to the team’s knowledge sharing and best practices. Qualifications: Advanced degree (Ph.D. preferred) in Computer Science, or a related field. Proven track record of building and innovations through publications or industry experience. Minimum of 6 years of experience in machine learning, with specific expertise in Large Language Models and Mixture of Experts. Strong programming skills in Python and machine learning frameworks like TensorFlow and/or PyTorch. Demonstrated ability to lead complex projects and work collaboratively in a team environment. Excellent problem-solving skills and a passion for innovation. Preferred Skills: Experience with cloud computing services (AWS, Azure, GCP). Knowledge of Big Data technologies (Hadoop, Spark). Familiarity with containerization and orchestration technologies (Docker, Kubernetes). Publications or presentations in recognized Machine Learning journals or conferences. We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth - Emphasizing transparency and honesty in every interaction and decision. Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits: We strive to provide competitive benefits to all employees. The benefits listed in this posting generally apply to U.S.-based employees. For employees hired outside the United States, benefits may vary based on local law, country-specific requirements, and the employment platform or entity through which the employee is hired. Competitive salary Comprehensive health, dental, and vision benefits package 401(k) match Equity options $200/month Health & Wellness stipend Continuing Education support $500/year Function Health subscription Free parking for in-office employees Flexible Time Off (FTO) Parental leave for eligible employees Supplemental life insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

Hadrian - Manufacturing the Future Hadrian is building autonomous factories that help aerospace and defense companies manufacture rockets, satellites, jets, and ships up to 10x faster and up to 2x cheaper. By combining advanced software, robotics, and full-stack manufacturing, we are reinventing how America produces its most critical parts. We’re accelerating our mission with the launch of Factory 3 in Mesa, Arizona, a 290,000-square-foot facility creating 350 new jobs. We are expanding rapidly to support thousands of future hires, launching Hadrian Maritime to expand into naval production, and introducing a Factory-as-a-Service model that delivers complete systems instead of individual parts. Hadrian is backed by leading investors including T. Rowe Price, Lux Capital, Founders Fund, and Andreessen Horowitz, our fast-growing team is united around reindustrializing American manufacturing for the 21st century and beyond. The Role: Hadrian is building advanced software systems to automate Design for Manufacturing (DFM) analysis and accelerate precision manufacturing. Our homebuilt DFM platform analyzes design prints and CAD models to make logical, data-driven determinations about whether and how Hadrian can manufacture a part. We work closely with some of the best operators in the world to identify high-impact opportunities where software, computer vision, and machine learning can meaningfully augment or automate complex engineering judgment. As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling these systems end to end. What You’ll Do: Research, develop and deploy cutting-edge deep learning models, including OCR, vision-language, and detection models for document understanding and layout analysis Contribute to the ongoing development and refinement of current models in close collaboration within the engineering team and end users Work in every facet of the Machine Learning lifecycle - including the creation and optimization of production data pipelines, and software systems for training, inference, labeling, and evaluation Judiciously combine open-source solutions and novel research to create world-class vision systems for manufacturing applications What We’re Looking For: Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or similar discipline), or an equivalent amount of deep learning experience 5+ years of experience in Computer Vision and/or Machine Learning, including ownership of projects throughout the entire ML Lifecycle Proficiency in Python, OpenCV, SQL, and one or more deep learning frameworks (PyTorch, Tensorflow, etc.) What sets you apart: A Master’s Degree in Computer Science with focus in Artificial Intelligence or Machine Learning Demonstrated experience with detection/segmentation models and achieving high performance results - including exploratory analysis, model selection, and hyperparameter tuning Previously worked in aerospace, defense, or manufacturing, and have experience working 3D/CAD/CAM data for manufacturing applications Strong software engineering skills and prior web development experience using Javascript/TypeScript or Python, and familiarity with FastAPI or Express frameworks Prior experience with distributed training using cloud infrastructure Prior experience with visual document understanding and layout analysis Published research or achieved SOA results on an ML application Prior experience with multi-modal deep learning models You have a passion for manufacturing and believe that the industry needs better software Prior experience working in a startup environment Compensation For this role, the target salary range is $160,000 - $250,000 (actual range may vary based on experience). This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs. Benefits for Full-time Employees Medical, dental, vision, and life insurance plans for employees 401k Relocation support may be provided for certain situations, based on business need. Flexible vacation policy Equity ITAR Requirements To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here . Hadrian Is An Equal Opportunity Employer It is the Company’s policy to provide equal employment opportunity for all applicants and employees. The Company does not unlawfully discriminate on the basis of race inclusive of traits historically associated with race (including, but not limited to, hair texture and protective hairstyles, such as braids, locks and twists), color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, transgender status, national origin (including, in California, possession of a drivers license), ancestry, citizenship, age, physical or mental disability, height or weight, medical condition, family care status, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, exercise of reproductive rights, any other basis protected by local, state, or federal laws, or any combination of the above characteristics. When necessary, the Company also makes reasonable accommodations for disabled candidates and employees, including for candidates or employees who are disabled by pregnancy, childbirth, or related medical conditions.

Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their fans and build a lasting business including: paid memberships, free memberships, community chats, live video, and selling to fans directly with one-time purchases. Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with: $10 billion+ generated by creators since Patreon's inception 100 million+ free memberships for fans who may not be ready to pay just yet, and 25 million+ paid memberships on Patreon today. We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Senior Machine Learning Engineer to support our mission. This role is based in San Francisco or New York as an in-office 2 days per week on a hybrid work model . About the Team You'll join the Relevance team, whose mission is to build the ML systems that power how fans discover creators and how content surfaces across Patreon. The team is responsible for search, ranking, feed relevance, and creator-fan matching. You'll work closely with a small, collaborative group of MLEs on shared infrastructure, code reviews, and roadmap alignment, while partnering cross-functionally with Product, Data Engineering, and Trust & Safety to deliver measurable impact across the platform . About the Role Conduct exploratory data analyses and proof-of-concept machine learning models to understand opportunities and potential project impact. Collaborate with cross-functional partners, such as product, engineering, design, legal, and trust and safety to design effective machine learning solutions. Analyze and prepare training data, including using crowdsourcing data labeling techniques. Train and iterate on machine learning models using novel techniques. Deploy machine learning models to production and write backend code when necessary to properly deploy the model. Debug models when observability shows performance gaps, and iterate on models. What you'll need Experience working in an end-to-end machine learning team environment (typically 5+ years): analyzing data, building and iterating on machine learning models, writing production-level code and shipping to production, monitoring performance, and A/B testing. Writes clean and robust code in Python or other programming languages, and provides substantive, constructive feedback in code reviews. Experience debugging complex systems with a systematic approach. Analyzes datasets thoroughly to develop product and customer insights. Writes clear technical documentation for both technical and non-technical audiences. Seeks and incorporates feedback on code, models, and technical approaches. Bachelor's degree in Computer Science, Computer Engineering, or a related field, or the equivalent We hire talented and passionate people from different backgrounds because workplace diversity and inclusion is critical to our ability to serve creators worldwide. If you’re excited about a role but your past experience doesn’t match with every bullet point outlined above, we strongly encourage you to apply anyway. If you’re a creator at heart, are energized by our mission, and share our company values, we’d love to hear from you. About Patreon Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts: Put Creators First | They’re the reason we’re here. When creators win, we win. Build with Craft | We sign our name to every deliverable, just like the creators we serve. Make it Happen | We don’t quit. We learn and deliver. Win Together | We grow as individuals. We win as a team. Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. If you need a reasonable accommodation during the interview process, please let us know via email at accommodations@patreon. Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, flexible time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching. Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office two days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates hired into remote-eligible roles are not expected to meet the same requirements. At Patreon, we believe in fair and transparent pay. In compliance with New York and California pay transparency laws, we are sharing the expected salary range for this role. The posted salary range is dependent on the location and the level. This range may encompass multiple levels within the role’s job family. The final offer will be based on candidate’s experience, skills, competencies, and geographic location, aligning with the appropriate job level within Patreon’s leveling framework. For remote employees located outside CA and NY, salary may vary based on location and local market conditions. Patreon reserves the right to modify or update compensation and benefits at any time

Imagine yourself designing and implementing the next breakthrough AI/Machine-Learning services, enabling AI for everyone at the start of the company's explosive growth. At super.AI, we are building services to enable AI/Machine Learning technologies for the masses. We are at the exciting stage where the growth of our company forces us to do aggressive hirings to realize our visions quicker. We are a group of ex-Google, Microsoft, Amazon, and MIT engineers and data scientists who spent the last 2 years creating the new programming interface for AI. What you'll do: Design/implement a system orchestrating machine learning workflows Build SDKs to enable engineers using our platform easily Build tools to enable our AI engineers to be efficient in developing new AI solutions Explore ideas for practical and reliable models for data label quality and various label supervision sources. What we are looking for: Smart, humble, hardworking, and collaborative By  smart  we don't necessarily mean high IQ (although a huge plus), but an affinity toward learning. We want to automate as much as possible so we can focus on the few things that require human intelligence and creativity. By  humble  we don't mean submissive or unambitious, we mean it in the same way Roger Federer is humble. By  hardworking , we don't mean long hours (be with your family). What we mean is while you're here you're doing the best work of your life. It means being disciplined, professional, focused, gritty, resilient, and resourceful. By  collaborative , we don't mean submissive or deferential, it means taking leadership from everywhere. "I'm taking responsibility to fix this system", if there's a lack of trust "I'm going to address it" if goals are unclear "I'm going to deal with it", it's about being a meritocracy All four of the above are required . We all know people missing one of these and it's not nice to work with them (e.g. someone who is smart and hardworking but not humble and collaborative). Paradoxical The people we like the most are smart but humble Wants to win no matter what, but will not cross lines of integrity to get there Strong beliefs weakly held, etc. 3+ years of software engineering experience Solid foundational knowledge of Python Familiarity with distributed system and cloud services (AWS, Azure, GCP) is a plus Solid understanding of machine learning principles Familiarity with container frameworks such as Docker and Kubernetes Experience with the latest machine learning infrastructure developments is a plus If this resonates with you, it would be amazing to get to know each other.

JOB SUMMARY: The research team at ECRS is looking for a talented, experienced individual who is fearless in tackling real world problems with efficient, practical Machine Learning (ML) solutions and demonstrate excellence in computer vision . RESPONSIBILITIES: Research, design, develop and train new machine learning models or adapt and apply existing models and applications. Design or develop software systems using design and functional specifications, scientific analysis, good software development practices, and mathematical models. Write, update, and maintain computer applications and software packages to handle specific jobs based on machine learning. Develop and direct software system testing and validation procedures. Review and modify existing software to correct errors, adapt to new hardware, and upgrade interfaces and improve performance. Direct software programming and development of corresponding documentation. REQUIRED QUALIFICATIONS: Advanced degree in Computer Science, Statistics, Mathematics or similar, or a comparable career in the industry with an exceptionally good record of successful projects. Minimum 3-5 years of industry experience in computer vision delivering successful solutions. Good understanding of machine learning, statistics, and mathematics. Experience and exceptionally good proficiency in Python and Java, or C/C++. Proficient knowledge and experience with image classification, detection, tracking, etc. Proficiency in image/video processing and computer vision tools. Proficiency in machine learning tools and frameworks such as PyTorch , TensorFlow or similar. Experience working with edge devices. Experience with modern deep learning techniques in CV including convolutional networks, residual networks, attentional models, etc. Familiarity with machine learning workflow. Familiarity with cloud-based machine learning platforms. Experience in software testing and debugging including the use of automated testing processes. Familiarity with relational databases. Familiarity with SQL. Basic understanding of a retail environment & operations . Strong written and verbal communication skills . PREFERRED QUALIFICATIONS: Experience with deep learning infrastructure is a plus. Experience with NLP is a plus. Proficiency in statistical analysis is a plus. Experience with parallel computing technologies or distributed computing techniques is a plus. ALL APPLICANTS MUST BE AUTHORIZED TO WORK IN THE UNITED STATES. ABOUT ECRS: ECRS is a fast-paced, progressive technology company with a wide range of opportunities for quality-oriented, career-minded individuals. Geographically situated in the heart of the Blue Ridge Mountains, ECRS offers the unique opportunity of a high-tech career in a resort college town setting. The ECRS family is made up of energetic, outgoing professionals who love what they do for a living. They are courteous, knowledgeable people who strive for excellence in everything they do. ECRS employees work together in dynamic teams to create, sell, install, and support our best-in-class retail automation solutions. We believe that acceptance of diversity is a key reason as to why we're successful. All qualified applicants who can demonstrate integrity and competence will receive consideration for employment and advancement without regard to race, color, religion, gender, sexual orientation, disability, age, political affiliation, or national origin.

Blanc Labs is a premier partner for global enterprises, leading the way in digitization, automation, and the development of next-generation digital products and services. Our expertise in digital transformation powers businesses to accelerate service delivery, drive customer engagement, and foster growth. We are looking for a Senior Machine Learning Engineer to lead the end-to-end development and lifecycle management of machine learning models powered by proprietary data. This is a highly impactful role at the core of our AI ecosystem, responsible for building scalable, production-grade ML systems that directly power intelligent product features. You will collaborate closely with Data Scientists, AI Engineers, and Principal AI Engineers to identify opportunities, develop robust models, and ensure seamless integration into production systems. Key Responsibilities Design, build, and deploy end-to-end machine learning models using proprietary datasets Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows Monitor model performance in production, ensuring accuracy, reliability, and relevance over time Troubleshoot model drift, data quality issues, and performance bottlenecks Qualifications Strong experience in machine learning engineering, with production deployment experience Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Experience with data preprocessing, feature engineering, and model evaluation techniques Hands-on experience deploying ML models as APIs or microservices Familiarity with MLOps practices, including model versioning, monitoring, and retraining pipelines Experience working with large-scale or proprietary datasets Strong software engineering fundamentals (testing, scalability, performance optimization) Ability to collaborate effectively with cross-functional teams Nice-to-Have Experience with cloud platforms (AWS, GCP, or Azure) and ML services (e.g., SageMaker, Vertex AI) Familiarity with containerization and orchestration tools (Docker, Kubernetes) Experience with real-time or low-latency ML systems Exposure to LLMs or hybrid AI/ML systems Experience in building data pipelines (Airflow, Spark, etc.) Blanc Labs is an equal opportunity employer and is committed to employing in accordance with the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act. Accommodations within reason due to a disability or medical need are available on request for candidates taking part in the recruitment process. Blanc Labs is enabling a digital future. Headquartered in Toronto, we partner with clients in North & South America to digitize and automate their operations and build their next generation of digital products and services. We empower clients to enhance their digital offerings and bring creative solutions to the market faster. Learn more at  www.blanclabs.com .

The Company Toters is an on-demand e-commerce and delivery platform and operates a service that enables customers to get anything in their city at the highest level of convenience. At Toters, technology is at the heart of everything we do. We have product teams that are working hard every day to create products that make our customers' lives easier. Our engineers are also continuously creating solutions to make our processes more efficient, all in an effort to get to our customers fast and at the best cost. If you are interested in working in a high growth startup environment, and look to be part of a team that will potentially change the way customers shop in the Middle East, apply now. About the Role We are seeking hands-on experienced Senior ML/AI Engineer  to join our Product Platform Engineering team. You will design and lead production-grade machine learning systems supporting quick commerce and fintech platforms. The role involves owning the full lifecycle of ML models, working with large-scale datasets (10TB+), and driving strategic AI initiatives that create measurable business impact. Key Responsibilities Build and maintain end-to-end ML pipelines using XGBoost, LightGBM, PyTorch, and TensorFlow. Architect scalable training and inference systems for real-time predictions . Lead cross-functional ML initiatives and mentor engineering teams. Optimize models for demand forecasting, fraud detection, and personalization . Apply MLOps best practices and collaborate with DevOps on deployment. Process and analyze 10TB+ datasets using distributed computing frameworks. Required Qualifications 5+ years building production ML systems at scale (100K+ users). Expert in XGBoost, LightGBM, and gradient boosting algorithms . Strong Python and ML stack experience: PyTorch, TensorFlow, NumPy, Scikit-learn. Hands-on experience with agentic AI systems and MCP frameworks. Proven track record in ML deployment and MLOps . Technical leadership and mentorship experience. Preferred Qualifications Proficiency in Python . Experience in quick commerce or fintech ML applications . Familiarity with cloud platforms, microservices, or distributed systems . Research, publications, or open-source contributions in ML/AI. What We Offer Competitive IC5-level compensation and benefits . Office-first hybrid setup with remote options for exceptional candidates. Clear path to Principal Engineer or ML Architect . Opportunity to work on high-impact AI projects with executive visibility. Collaborative environment with mentorship and career growth opportunities .

About Niyam IT (Niyam) Niyam was founded in 2007 by a group of consultants who shared a unique vision: a technology company steeped in orderly process yet driven by passion and innovation. Over the following decade, we fine-tuned our craft and built an impressive track record of successful outcomes, securing our reputation as the go-to provider of smart, innovative solutions. Today, Niyam is at the forefront of the industry, leading the way in crafting mission-critical technologies for Emergency Preparedness & Response, Natural Resource Management, Law Enforcement & Justice, Health IT, and Global Citizen Services. What We Offer: Flexible Work Hours : Life doesn’t always fit into a 9-to-5 schedule. We offer flexibility to help you manage your work-life balance effectively. Remote Work : Niyam understands the value of flexibility. We offer remote work. Career Growth : Niyam is not just a job; it’s a career journey. We provide a supportive environment for your professional development and offer fully paid opportunities for training and advancement within the company. Great People : Our people are the blueprint of who Niyam is to the industry and community. Great Environment : Niyam fosters a great environment where innovation, collaboration, and personal growth thrive. Diversity & Inclusion : We believe in the strength of diverse perspectives. Your unique ideas are welcomed and celebrated every day at Niyam. Join us in creating a workplace where innovation, diversity, and well-being thrive. Your journey at Niyam awaits. Apply today! Niyam is seeking an AI/ML Engineer to join our team in support of our work with a federal client. This role will focus on designing, developing, and deploying advanced AI/ML solutions that enhance operational efficiency, enable data-driven decision-making, and support evolving mission needs. The ideal candidate brings a strong foundation in machine learning, data engineering, and MLOps, along with experience working in secure, regulated environments. This position requires collaboration across cross-functional teams and a commitment to delivering scalable, ethical, and compliant AI solutions. We offer competitive compensation and benefits. This full-time position will be hybrid to Ashburn, VA. This position is contingent upon award of contract. Roles and Responsibilities Design, develop, train, and validate advanced AI and machine learning models to support mission-critical use cases for a federal client, ensuring alignment with operational objectives and data governance standards. Evaluate and select appropriate machine learning techniques, algorithms, and neural network architectures (e.g., supervised, unsupervised, deep learning), leveraging frameworks such as TensorFlow and PyTorch to build scalable and efficient solutions. Perform end-to-end data lifecycle activities, including data collection, ingestion, cleansing, preprocessing, and feature engineering, ensuring data quality, integrity, and compliance with federal data management policies. Deploy AI/ML models into production environments, integrating with existing enterprise systems, cloud platforms, and APIs while ensuring high availability, scalability, and security. Establish and maintain MLOps pipelines to support continuous integration, continuous delivery (CI/CD), automated testing, model versioning, and performance monitoring across the model lifecycle. Monitor model performance over time, implement retraining strategies, and optimize models to ensure sustained accuracy, reliability, and operational effectiveness in dynamic environments. Identify, assess, and mitigate bias in datasets and model outputs, ensuring fairness, transparency, and adherence to ethical AI principles and applicable federal guidelines. Collaborate with cross-functional teams, including data engineers, software developers, cybersecurity personnel, and program stakeholders, to translate mission and business requirements into technical AI/ML solutions. Document model development processes, methodologies, and results to support auditability, reproducibility, and compliance with federal standards and accreditation requirements. Support security and compliance initiatives by aligning AI/ML solutions with frameworks such as NIST and FedRAMP, ensuring proper handling of sensitive data and adherence to system authorization processes (e.g., ATO). Qualifications and Education Requirements: US Citizenship with ability to obtain a Public Trust. Bachelor’s degree or higher in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical discipline from an accredited institution. 8+ years of progressive experience designing, developing, and deploying machine learning or artificial intelligence solutions within enterprise or mission-driven environments. Demonstrated experience with machine learning frameworks and tools such as TensorFlow, PyTorch, Scikit-learn, or similar technologies. Hands-on experience with data preprocessing, feature engineering, and working with large, complex datasets in both structured and unstructured formats. Proven experience deploying and operationalizing AI/ML models in production environments, including integration with cloud platforms (e.g., AWS, Azure, or GCP). Experience implementing MLOps practices, including CI/CD pipelines, model monitoring, versioning, and lifecycle management. Strong understanding of responsible AI practices, including bias detection, model explainability, and ethical AI considerations. Familiarity with federal security and compliance frameworks (e.g., NIST, FedRAMP) and experience working within regulated environments is preferred. Strong analytical, problem-solving, and communication skills, with the ability to effectively collaborate across technical and non-technical stakeholders. Local to Ashburn, VA and available to work onsite as needed. Preferred Skills: Experience supporting federal agencies or working within government contracting environments. Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes). Experience with big data technologies such as Hadoop, Spark, or distributed data processing frameworks. Familiarity with API development and microservices architecture. Experience implementing AI/ML solutions in cloud-native environments. Application Deadline:   This position will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. Niyam is an Equal Opportunity (“EEO”) Employer. All qualified applicants will receive consideration without regard to race, color, creed, religion, sex, sexual orientation, gender identity, pregnancy, marital status, partnership status, age, citizenship status, veteran or military status, medical condition, genetic information, national origin, disability, unemployment status or any other characteristic prohibited by federal, state and/or local laws.    If you require a reasonable accommodation due to a disability to complete your application, or if you face challenges using our online application system and need an alternative way to apply, please reach out to us at +1 703.429.2450 or email hr@niyamit.com.

Do you want to build a scalable platform that makes qualitative insights fast, easy and valuable together with an international Tech team with competent and experienced colleagues? And are you proficient in GoLang or Python? Then this role as Backend Engineer is perfect for you. Who are we? GetWhy is the end-to-end AI-powered human insights platform, with the mission to bring companies and customers closer together and root empathy into the decision-making process. We’re a global insights provider, that with empathy at our core, redefines the boundaries of qualitive research and emphasizes the value of being truly consumer-centric. For the past decade, we’ve strived to put more empathy into the world by bridging the gap between companies and consumers, to create a better understanding and cater to human experiences on a global scale. More than that, GetWhy is a dynamic scale-up with a can-do attitude, located in the heart of Copenhagen, and rapidly growing to meet the demands of our international pool of clients. To fuel our journey, we just raised a $34.5m Series A investment from PeakSpan Capital, the largest round outside Life Science in Denmark. What does the work consist of? We are looking for a coworker who can further develop our platform together with your colleagues in Engineering and Product. You will be utilizing your coding skills in a crossfunctional team of Backend Developers, Data Scientists, Frontend Developers and an Infrastructure Engineer. You will be reporting to our Lead Engineer, who have extensive experience building insights-focused solutions and platforms. Our tech stack consists of AWS, GoLang, PHP Laravel, SQL, Mongodb, Vue/nuxt, Javascript as well as Docker and Container orchestration. What qualifications do you need? We are looking for a person who is motivated to be part of a growth journey with an ambitious and enthusiastic Engineering Team. We expect you to: Have some experience working with backend development in GoLang. Have a broad interest in web technologies in general Have motivation for working in a scaling and fast-paced environment Be as strong believer in teamwork Be professionally proficient in English Live in, or in proximity of, Copenhagen What we Offer A product that you believe in, where we truly help our customers get meaningful and valuable insights from their customers. A vibrant and international work environment with 50 colleagues across 15+ nationalities. A flexible work schedule with opportunities to work from home. A scale-up company with the ambition and potential to become a global leader Personal learning and development. We want to set the groundwork to skyrocket your career, our company is built upon everyone’s ideas and desire to progress. Competitive compensation plans along with pension A newly renovated pet-friendly office in the heart of Copenhagen, with a stunning waterfront view, relaxation areas, free daily energizers, vitamin shots, snacks, drinks, and a healthy and flexible lunch. Annual company retreats and lots of Friday bar events. We work hard, we party hard. A full-time position at Sundkaj 125, 2150 Copenhagen with a starting date as soon as you can. ...and of course, a great team spirit! How do I apply? The only thing you have to do is click on the Easy Apply button and upload your resumé or LinkedIn profile. Do we see a potential match, we will invite you in for a talk in our office to get to know you even better and to tell you more about our company, culture and the role. So, what are you waiting for? Hit the apply button and let us take the first step in our journey together. All the best, GetWhy

We are expanding our B2B consultant network with AI & Machine Learning Engineers (Python, Cloud, Git, RAG, MLOps) experienced in machine learning, applied AI solutions, and intelligent system development. Engagements may involve model development, AI integration into existing systems, optimization of ML pipelines, and applied generative AI (GenAI) use cases. If you are an independent AI professional open to flexible project-based work, we would be glad to stay connected. We are continuously building our network of experienced B2B consultants and contractors across various technology domains. By applying to this talent pool, you are expressing interest in future project-based collaborations with our team. When a suitable opportunity aligned with your expertise becomes available, we will reach out to discuss potential engagement. Please note:   These opportunities are exclusively for independent consultants and B2B contractors (freelance / company-to-company cooperation), and full-time employment roles. If you are open to flexible, project-driven collaboration, we would be glad to stay connected.

About Us: KinaTrax's mission is to provide professional and collegiate teams with game-changing insights about their most valuable asset: their athletes. We deliver research-grade markerless motion capture technology that allows teams to collect in-game biomechanical performance data on their athletes. KinaTrax camera systems are currently deployed in over 50 stadiums & labs across MLB , MiLB , & NCAA organizations - and expanding rapidly. Our comprehensive data capture & analysis tools are operationalized for daily use by players, GM's, coaches, trainers, medical staff, and beyond. As the market leader in Major League Baseball, KinaTrax has established itself as a foundational part of the teams' day to day strategy and decision-making machine. But we aren't finished. We are constantly innovating and looking to recruit talented teammates to help us continue to revolutionize this space. Your contributions will focus on bringing the next generation of athlete performance data, across a variety of sports, to teams worldwide. What we are looking for: Currently we are seeking a highly motivated candidate to lead the development of our core technologies that measure athletic performance in competitive environments. You will serve a central role in building and refining our computer vision and machine learning algorithms focused on tracking athlete movement and biomechanics. Joining at our early stage of growth will enable you to become a key figure as we seek to expand our software development team & capabilities. Ambitious individuals will have the opportunity to assume a central leadership role as the team develops, working alongside the core leadership team to define the future product roadmap and contribute to the overall company strategy. Our Stack Machine Learning: Proficient in Python, TensorFlow, PyTorch Computer Vision: Highly skilled in C++, OpenCV DevOps: Visual Studio, Github, Bazel, CMake, FFMPEG User Interface: Qt Roles & Responsibilities The primary responsibility of this role is to lead the day to day development & productizing of our Computer Vision/Machine Learning technologies from conception to deployment Own the development of system requirements and product architectures Adapt and improve existing algorithms for new environments, activities & applications Lead the recruitment & expansion of our ML/CV and software development teams Provide technical leadership and guidance to both your team members and your project peers Operate strategically and tactically. Working closely with senior leadership to define and execute the long-term strategies and product roadmaps and help set direction while staying on top of the day to day software development. Key Qualifications PhD/MS degree in Computer Vision, Machine Learning or related field with research publications; alternatively equivalent years of industry experience solving problems which do not have readily available solutions. Expertise in 3D computer vision, machine learning, and artificial intelligence Experience maintaining and promoting best practices for software development, for example: test driven design/development, unit tests, code coverage, refactoring, gated checkins, code reviews, continuous integration etc. Expertise in at least one of these specific areas: 2D/3D object(s) detection and tracking, keypoint detection, human pose recognition, image sequence/ visual/LIDAR-based tracking, and multi-object tracking, optimization, computational geometry Track record of driving research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization Demonstrated experience in recruiting and managing technical teams, including performance management. What will set you apart 5-7+ years industry or applied experience Experience deploying cloud computing solutions Container eco-systems (Kubernetes) Creative projects evaluating humans in natural environments Demonstrable interest in sports/athletic performance/biomechanics Exposure to biomechanics Experience with embedded systems Creative sensor fusion projects/solutions Experience with pose2image translation

Are you ready to take your Machine Learning skills to the next level? We are hiring a Founding Machine Learning Engineer to own the intelligence layer of our products. You will design, build, and improve the models and decision systems behind recommendations, ranking, personalisation, retrieval, agent behaviour, and selected predictive analytics use cases. You will work directly with the founders to turn ambiguous product ideas into production systems that create measurable customer value. In a team of our size, this is an end-to-end role: you may touch data exploration, modelling, evaluation, experimentation, and production iteration in the same week. This is not a pure research role. We care about people who can move from data and hypotheses to shipped systems and business impact. All you need is: Strong foundations in machine learning, statistics, computer science, or a similar quantitative discipline; Experience building and shipping ML systems or intelligent product features in production or near-production environments; Strong Python skills and comfort working across data, modelling, evaluation, and production collaboration; Good understanding of experimentation, model evaluation, feature engineering, data quality, and error analysis; Clear communication and the ability to work through messy, ambiguous product problems; High ownership, self-direction, and a strong bias toward action; 5+ years building and shipping ML systems or intelligent product features in production; Strong understanding of model evaluation, cross-validation, feature engineering, and data quality challenges in real-world environments; Experience working with large-scale behavioural, transactional, or contextual data; Strong software engineering habits, including writing clean, testable, maintainable Python code. What would be an advantage: Experience with recommendation systems, ranking, search, personalisation, or marketplace/feed optimisation; Experience with LLM applications, RAG, GenAI agents, prompt iteration, or evaluation of GenAI systems; Experience running A/B tests or online experiments; Experience working closely with product teams and translating user problems into ML solutions; Experience with real-time ML, streaming features, low-latency inference, or online learning; Experience with causal inference, uplift modelling, multi-armed bandits, or other decision-optimisation methods; Familiarity with cloud ML infrastructure, containerised deployment, and MLOps workflows; Experience in iGaming, fintech, e-commerce, or another domain with large-scale transactional and behavioural data; Experience with predictive analytics use cases such as segmentation, churn prevention, LTV modelling, or opportunity prioritisation. Your daily adventures will look like: Design, build, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows; Turn product goals into concrete ML problems, evaluation plans, experiments, and shipped features; Work with behavioural, transactional, contextual, and unstructured data to identify signals and improve model quality; Build offline evaluation frameworks and online experiments to measure relevance, quality, latency, cost, and business impact; Improve GenAI agent behaviour through better retrieval, context management, prompting, tool use, orchestration, and evaluation; Investigate failure modes, run error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity; Partner closely with platform and backend engineers to deploy, monitor, and iterate on models in production; Help define how the company does ML: metrics, experimentation discipline, technical standards, and long-term direction; Work with real-time behavioural and transactional signals to improve recommendations, personalisation, and intelligent product behaviour; Contribute to predictive and insight-driven ML use cases such as segmentation, churn prediction, recommendation measurement, and opportunity ranking; Write clean, testable Python and contribute reusable ML components and shared libraries used across the platform. What Success Looks Like in the First 6 Months: You ship meaningful improvements to a recommendation, personalisation, or GenAI workflow used in production; You establish a practical evaluation framework for one or more core ML systems; You turn ambiguous product opportunities into clear experiments and sound technical decisions; You improve at least one metric that matters, such as relevance, task completion, conversion, retention, latency, or cost efficiency; You become a trusted owner who spots high-leverage ML opportunities and drives them forward without needing detailed instruction; You help establish a repeatable approach to experimentation, model iteration, and production-quality ML development. And this is how our interview process goes: A 30-minute interview with a member of our HR team to get to know you and your experience; A 1-hour technical interview; A final interview to gauge your fit with our culture and working style. Sounds interesting? Do not hesitate to apply or contact us if you have any questions!

We are looking for a Machine Leaning Engineer (MLE) to design, build, and optimize our machine learning operation. You will play a crucial role in scaling AI models from research to production, ensuring smooth model deployment, monitoring, and lifecycle management across our Google Cloud Platform (GCP) infrastructure. You'll work closely with data scientists, ML Ops, and data engineers to automate workflows, improve model performance, and ensure reliability for our AI that serves millions of players worldwide. What You'll Do Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference. Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards. Optimize ML model inference performance to improve latency and cost-efficiency of AI applications. Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform, including proactive identification and resolution of issues related to model performance and data quality. Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system. Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming. What We're Looking For 1+ years of experience as an ML Engineer, with a focus on developing and deploying machine learning models in production environments. Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow). Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services. Experience building and deploying scalable data pipelines and machine learning models in production environments. Understanding of model monitoring, logging, and observability best practices for ML models and applications. Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is a bonus. Bonus experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.

ABOUT SOFASCORE Sofascore is a sports-tech company created with one goal in mind – giving sports enthusiasts a deeper understanding of the game. Our platform is the leading provider of advanced sports insights. From the biggest derbies to amateur matches, every game counts - that’s why we have the largest data coverage with 20,000+ tournaments across 25 sports. This comes easy with Torneo, our very own tournament management software for lower leagues. The global recognition of the Sofascore Rating, along with the Player of the Season award for the highest-rated players, positioned us as the authority in evaluating player performance. The Sofascore team counts more than 300 experts in 20 teams, primarily playing at our home court in Croatia, but we also have talents showing their skills worldwide. More about the company /// More about the platform ABOUT THE ROLE At Sofascore, we are passionate about redefining the sports experience for millions of users worldwide through cutting-edge technology. We are looking for an experienced Senior Machine Learning Engineer to join our AI Team. This is a key role for someone eager to apply advanced ML techniques in production at scale, contributing to projects that directly impact our users: recommender systems, personalized feeds, sentiment analysis, and semantic search. As a Senior ML Engineer, you will be responsible not only for developing state-of-the-art models but also for ensuring their robustness, scalability, and seamless deployment into production environments. You will work closely with other engineers, data scientists, and product teams to deliver intelligent, reliable, and high-performing solutions. Your responsibilities Design, build, and deploy production-ready ML systems with high reliability and scalability. Develop, evaluate, and optimize recommender systems, feed systems, and NLP-driven solutions (e.g., sentiment analysis, semantic search). Implement robust MLOps practices using Docker, Kubernetes, and cloud platforms. Collaborate on system design, data pipelines, and end-to-end lifecycle management of ML models. Write clean, maintainable, and high-quality code following engineering best practices. Mentor and support less experienced colleagues, setting standards for technical excellence within the team. What we are looking for 5+ years of industry experience working on machine learning projects, with a proven track record of delivering models into production. Strong knowledge of recommender systems, NLP methods, and modern ML architectures (including transformers and LLMs). Proficiency in Python (or similar), with excellent coding and debugging skills. Hands-on experience with containerization, orchestration, and deployment technologies (Docker, Kubernetes). Strong understanding of MLOps, CI/CD pipelines, monitoring, and model lifecycle management. Ability to design scalable solutions, from prototype to production. Strong problem-solving mindset, curiosity, and passion for innovation. What sets you apart Experience working on large-scale consumer-facing ML systems. Familiarity with cutting-edge LLM applications and their integration into production systems. Research background, evidenced by impactful contributions, publications, or applied innovations. Experience mentoring and providing technical leadership in a collaborative team environment. What we offer The opportunity to work with a cutting-edge sports platform impacting millions worldwide Family benefits package Education - internal and through international conferences and workshops Top quality equipment and budget for Mobile phone Paid package of general physical examination once a year Sofascore Canteen (lunch options) Sofascore Bar (coffee and drinks on us) Numerous other benefits that we would verbally communicate to you

Sitero is a next-generation clinical trial solutions partner, working with more than 200 Pharmaceutical, Biotech, and Institutional Research Organizations globally. We use technology to drive safety, compliance, quality, and efficiency in research and clinical trials, helping customers bring life-changing treatments to market safer and faster. Driven by a mission to advance clinical research through a technology-enabled delivery model, Sitero leverages a deep well of knowledge across Technology, Clinical Operations, Biosafety, and Drug Safety. We are looking for engineers who are versatile, display leadership qualities, and are enthusiastic to take on new problems across the full stack as we continue to push technology forward. Job Title: Software Engineer, Machine Learning Location: Ontario, Canada (Mississauga area preferred) Why Sitero? At Sitero, you will work on projects that have a direct impact on human health. We offer a fast-paced environment where you can switch teams and projects as our business grows. We are an equal opportunity workplace committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. About the Role We are seeking a Software Engineer, Machine Learning to lead the development of next-generation AI technologies that power our clinical trial platforms. In this role, you will sit at the intersection of AI infrastructure, model optimization, and large-scale software engineering. You will be responsible for building scalable data pipelines for AI, optimizing model performance on hardware accelerators, and deploying state-of-the-art Large Language Models (LLMs) and Multi-Modal models that revolutionize how clinical data is processed and analyzed. You will join a collaborative team that addresses unique problems focused on maximizing scientific and real-world impact. You will not only build AI but also embody the future of engineering by using AI to accelerate your own workflows—leveraging the very tools we build and integrate to augment software development practices. Responsibilities AI & ML Infrastructure: Design, develop, and deploy large-scale ML infrastructure and state-of-the-art AI solutions (e.g., LLMs, Multi-Modal models) to enhance clinical trial efficiency and safety. Systems Architecture: Serve as a technical leader for the design of high-throughput, low-latency data pipelines and storage solutions (data loading, caching, vector stores) required for massive datasets. AI-Augmented Engineering: Actively utilize and advocate for AI-assisted development tools (e.g., coding assistants, automated refactoring agents) to accelerate the software development lifecycle, generate boilerplate, write tests, and optimize legacy codebases. Cross-Functional Leadership: Collaborate with clinical operations, product, and research teams to translate complex business requirements into robust engineering solutions. Establish alignment on technical strategy and influence the direction of Sitero’s AI platforms. Mentorship: Mentor engineers across the organization, fostering best practices in distributed systems, ML engineering, and the adoption of AI-native development workflows. Minimum Qualifications Bachelor's degree in computer science, Engineering, or a related technical field, or equivalent practical experience. 8+ years of experience in software development with proficiency in Python, C++, or similar languages. 7+ years of experience leading technical project strategy and optimizing industry-scale ML infrastructure (model deployment, evaluation, fine-tuning). 2+ years of experience with state-of-the-art AI techniques (LLMs, RAG, Computer Vision) and frameworks (PyTorch, TensorFlow, JAX). Demonstrated proficiency in using AI models to augment software engineering practices—specifically using LLMs for code generation, debugging, architectural design validation, and documentation. Experience designing and scaling data infrastructure, including distributed storage systems, data lakes, or high-performance ETL frameworks. Preferred Qualifications Master’s degree or PhD in Computer Science, Artificial Intelligence, or related technical field. Experience with hardware/software to co-design and optimize models for specific accelerators (GPUs, custom silicon). Background in the Life Sciences, Pharmaceutical, or Clinical Research industries. Familiarity with modern data storage formats (Parquet, ORC) and vector database technologies. Proven track record of technical leadership in a complex, matrixed organization, managing cross-functional projects.

Join FxPro : a leading international fintech company. Be a part of our expanding international team, with offices in Limassol, London, Nassau, Dubai, and Yerevan. FxPro boasts a diverse workforce of over 600 employees representing 50 nationalities, making it an exciting and dynamic workplace. At   FxPro , we see each team member as an integral part of our success story. As a Machine Learning Engineer you will develop new ML models and collaborate with the Dealing, Business Analysts and our BI Experts. Responsibilities: Develop and deploy machine learning models for trader profiling and other business applications Collect, clean, and preprocess large datasets, applying feature engineering and transformation techniques Monitor model performance in production and retrain models as needed Integrate models into operational systems, ensuring scalability, security, and optimal performance Collaborate with cross-functional teams to align model development with business needs Continuously optimize models and stay current with the latest advancements in ML research Requirements: Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or a related field, or equivalent practical experience 3+ years of experience in machine learning, with a proven track record of developing and deploying models in production Strong understanding of mathematical statistics and probability theory Proficiency in Python and machine learning libraries (e.g., pandas, NumPy, Scikit-Learn) Solid understanding of machine learning algorithms (e.g., regression, classification, clustering) Familiarity with MLOps tools (e.g., Airflow, MLflow, DVC), containerization (e.g., Docker), and orchestration (e.g., Kubernetes) Experience with SQL, including complex queries, analytic functions, and querying large datasets. Familiarity with model monitoring and logging tools (e.g., Prometheus, Grafana) Knowledge of cloud platforms (Azure, AWS, GCP) Experience with version control and CI/CD practices (e.g., Git, Jenkins) Strong problem-solving and analytical skills A self-driven attitude with a strong sense of ownership and the ability to work independently to solve problems and develop solutions Excellent command of the English language, both written and verbal Interview steps Recruiter screen   (30 minutes) Technical Interview   (1 hour) Test task Technical Interview (1 hour) Final Interview   with manager (30 minutes) Our benefits Excellent compensation package Medical insurance Provident fund In-house gym with a personal trainer Free daily lunch catering, snacks, and beverages Company discount card for various products & services 21 days of a nnual leave and 10 days of sick leave annually Shuttle bus service from Limassol Birthday gift Relocation bonus and visa/work permit support

📍 San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite Machine Learning Infrastructure Engineer Location: Onsite in San Francisco Compensation: Competitive Salary + Equity Who We Are UniversalAGI is building OpenAI for Physics. AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production. We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you. About the Role UniversalAGI is hiring an Infrastructure Engineer to build and own the execution platform powering our research and customer deployments: data generation + simulation orchestration + training/fine-tuning infrastructure + benchmarking pipelines + production deployments in customer environments. You’ll work closely with the CEO and founding team to turn research into repeatable, scalable, reliable systems - internally and in customer infrastructure. This is a “ship outcomes” role: your work directly determines how fast we can iterate, how reproducible our results are, and how reliably we deliver in production. What You’ll Do Build the foundation platform (internal) Build and operate scalable infrastructure for data generation and simulation workflows (job orchestration, scheduling, queues, retries, observability). Build reproducible pipelines for training/fine-tuning and benchmarking (artifact/version management, experiment tracking, dataset lineage). Own cost/performance tradeoffs across compute, storage, networking, and runtime efficiency. Deploy to customers (external) Lead deployments of our stack into customer cloud/on-prem environments, including secure networking, permissions, and data movement. Build robust deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response. Partner with customers to ensure reliability and repeatability under real-world constraints (security, compliance, infra limits, data governance). Qualifications Strong software engineering skills (clean code, debugging, reliability, reproducibility). Hands-on experience building/operating infrastructure for ML/compute-heavy workflows: pipelines, job orchestration, GPU compute, storage, CI/CD, monitoring. Olympic athlete mindset : You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering to customers. Resourcefulness : you know when to do the “quick & correct” fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/ Ownership : Comfortable owning work end-to-end and being accountable for measurable outcomes. Bonus Qualifications Experience with workflow orchestration (e.g., Ray, Kubernetes, Slurm). Experience with GPU infrastructure and distributed training systems. Experience building evaluation/benchmarking frameworks with strong reproducibility guarantees. Experience deploying into regulated / security-sensitive environments (gov/defense/enterprise). Experience with simulation/HPC pipelines (CFD, meshing, batch workloads) is a plus but not required. Experience in an FDE-style / delivery execution role (or similar “ship results fast” environments). Cultural Fit Technical Respect : Ability to earn respect through hands-on technical contribution Intensity : Thrives in our unusually intense culture - willing to grind when needed Customer Obsession : Passionate about solving real customer problems, not just publishing papers Deep Work : Values long, uninterrupted periods of focused work over meetings High Availability : Ready to be deeply involved whenever critical issues arise Communication : Can translate complex model decisions to customers and team Growth Mindset : Embraces the compounding returns of intelligence and continuous learning Startup Mindset : Comfortable with ambiguity, rapid change, and wearing multiple hats Work Ethic : Willing to put in the extra hours when needed to hit critical milestones Team Player : Collaborative approach with low ego and high accountability Bias for Action : Ships experiments fast, learns from failures, and iterates quickly What We Offer Opportunity to define the future of physics AI from the ground up Work on cutting-edge problems at the intersection of deep learning and physics simulation Direct collaboration with the founder & CEO and ability to influence company strategy Competitive compensation with significant equity upside In-person first culture - 5 days a week in office with a team that values face-to-face collaboration Access to world-class investors and advisors in the AI space Benefits We provide great benefits, including: Competitive compensation and equity. Competitive health, dental, vision benefits paid by the company. 401(k) plan offering. Flexible vacation. Team Building & Fun Activities. Great scope, ownership and impact. AI tools stipend. Monthly commute stipend. Monthly wellness / fitness stipend. Daily office lunch & dinner covered by the company. Immigration support. How We’re Different “The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly." - Teddy Roosevelt At our core, we believe in being “in the arena. ” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful. If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.

About DeepWalk DeepWalk is a fast-growing venture that helps cities keep people safe using computer vision to map and monitor their sidewalks. We have ongoing contracts with cities, universities, and engineering firms. In the past year, we’ve processed thousands of miles of sidewalk across 20+ states , generating millions of labeled data points used in real-world infrastructure decisions. We’ve raised $4.1M, recently closed a $2.1M seed round led by Enable Ventures, and we currently generate 7-figure revenue helping communities across America. Staff Software Engineer — Computer Vision Platform $150,000 – $190,000 base salary + equity · Hybrid (Chicago) We’re hiring a Staff Software Engineer to lead the technical direction of DeepWalk’s computer vision platform for automated sidewalk inspection. This role is focused on tackling high-impact, open-ended problems around large-scale imagery, production ML systems, and data pipelines that handle hundreds of terabytes of data. We’re specifically looking for engineers who have built and operated computer vision or ML systems in production at scale (not just research or prototyping). You’ll play a key role in how we process, analyze, and deliver insights from millions of images, supporting cities as they work to build safer, more accessible infrastructure. In this role, you’ll work closely with a handful of experienced engineers, help set technical direction, and establish patterns the team can scale with. Our core stack today includes Python-based CV/ML systems running on AWS, with data pipelines and services designed to handle large volumes of imagery and geospatial data. What You’ll Do As a Staff Engineer at DeepWalk, you’ll have significant ownership over both the systems we build and how we build them. You will… Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration Improve model performance in real-world conditions (noise, edge cases, data drift) Design and improve data pipelines that process thousands of miles of sidewalks and millions of images Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance Set and evolve best practices around deployment, observability, system reliability, and scalability Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team Work closely with the CEO and operations team to turn business needs into clear technical priorities What Success Looks Like (First 3–6 Months) Build a strong understanding of our current computer vision systems and data pipelines Identify the biggest scaling, reliability, and maintainability gaps as we grow Propose and begin executing on a clear plan to improve how we deploy, monitor, and operate production ML systems Become a trusted technical partner to leadership What We’re Looking For Required 6+ years of experience, including direct ownership of production ML or computer vision systems (training, deployment, and ongoing operation) Experience taking models from training → production → monitoring → iteration in a real-world environment Experience owning system architecture and influencing technical decisions across teams Experience deploying computer vision or ML systems in real-world production environments Fluency in at least one modern backend language (Python, Java, TypeScript, Go, etc.) Strong understanding of system design, scalability, and distributed systems Experience with cloud platforms such as AWS, GCP, or Azure Comfort working in a startup or growth-stage environment with changing requirements Nice to Have Experience with large-scale or real-time data pipelines Infrastructure-as-Code experience (Terraform, CDK, etc.) ML observability, data validation, or model deployment experience Familiarity with geospatial, imagery, or sensor-driven data An interest in urban planning, accessibility, or civic technology Compensation & Benefits $150,000 – $190,000 base salary, based on scope and experience Equity Unlimited PTO (most of our team takes 3–4 weeks per year) Health insurance 401(k) with ~4% match (100% on first 3%, 50% on the next 2%) Convenient office location in The Loop Hybrid work environment (typically 2 days in-office) DeepWalk participates in the federal E-Verify program to confirm the employment eligibility of all newly hired employees. DeepWalk is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected status. In accordance with applicable laws, DeepWalk provides equal pay for equal work and complies with all federal, state, and local pay transparency and compensation requirements.

About DeepWalk DeepWalk is a fast-growing venture that helps cities keep people safe using computer vision to map and monitor their sidewalks. We have ongoing contracts with cities, universities, and engineering firms. In the past year, we’ve processed thousands of miles of sidewalk across 20+ states , generating millions of labeled data points used in real-world infrastructure decisions. We’ve raised $4.1M, recently closed a $2.1M seed round led by Enable Ventures, and we currently generate 7-figure revenue helping communities across America. Senior/Staff Software Engineer — Computer Vision Platform $150,000 – $190,000 base salary + equity · Hybrid (Chicago) We’re hiring a Senior/Staff Software Engineer to lead the technical direction of DeepWalk’s computer vision platform for automated sidewalk inspection. This role is focused on tackling high-impact, open-ended problems around large-scale imagery, production ML systems, and data pipelines that handle hundreds of terabytes of data. We’re specifically looking for engineers who have built and operated ML or computer vision systems in production at scale (not just research or prototyping). You’ll play a key role in how we process, analyze, and deliver insights from millions of images, supporting cities as they work to build safer, more accessible infrastructure. In this role, you’ll work closely with a handful of experienced engineers, help set technical direction, and establish patterns the team can scale with. Our core stack today includes Python-based CV/ML systems running on AWS, with data pipelines and services designed to handle large volumes of imagery and geospatial data. What You’ll Do As a Staff Engineer at DeepWalk, you’ll have significant ownership over both the systems we build and how we build them. You will… Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration Improve model performance in real-world conditions (noise, edge cases, data drift) Design and improve data pipelines that process thousands of miles of sidewalks and millions of images Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance Set and evolve best practices around deployment, observability, system reliability, and scalability Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team Work closely with the CEO and operations team to turn business needs into clear technical priorities What Success Looks Like (First 3–6 Months) Build a strong understanding of our current computer vision systems and data pipelines Identify the biggest scaling, reliability, and maintainability gaps as we grow Propose and begin executing on a clear plan to improve how we deploy, monitor, and operate production ML systems Become a trusted technical partner to leadership What We’re Looking For Required 5+ years of experience, including direct ownership of production ML or computer vision systems (training, deployment, and ongoing operation) Experience taking models from training → production → monitoring → iteration in a real-world environment Experience owning system architecture and influencing technical decisions across teams Experience deploying ML systems in real-world production environments Fluency in at least one modern backend language (Python, Java, TypeScript, Go, etc.) Strong understanding of system design, scalability, and distributed systems Experience with cloud platforms such as AWS, GCP, or Azure Comfort working in a startup or growth-stage environment with changing requirements Nice to Have Experience with large-scale or real-time data pipelines Infrastructure-as-Code experience (Terraform, CDK, etc.) ML observability, data validation, or model deployment experience Familiarity with geospatial, imagery, or sensor-driven data An interest in urban planning, accessibility, or civic technology Compensation & Benefits $150,000 – $190,000 base salary, based on scope and experience Equity Unlimited PTO (most of our team takes 3–4 weeks per year) Health insurance 401(k) with ~4% match (100% on first 3%, 50% on the next 2%) Convenient office location in The Loop Hybrid work environment (typically 2 days in-office) DeepWalk participates in the federal E-Verify program to confirm the employment eligibility of all newly hired employees. DeepWalk is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected status. In accordance with applicable laws, DeepWalk provides equal pay for equal work and complies with all federal, state, and local pay transparency and compensation requirements.

Help us push the boundaries of what's possible in LLM post-training. If you love training models, exploring new architectures, running experiments, and turning research insights into products that ship, we'd love to meet you. About Inference.net Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting. We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates. About the Role You will be responsible for conducting research into experimental models, training systems, and modalities to create novel products for our customers. Your work will span from exploring new architectures and learning methods to optimizing latency and efficiency, with the goal of delivering better models to customers. Your north star is pushing the frontier of what's possible in LLM post-training. You'll explore new techniques, run rigorous experiments, and when something works, help bring it into production with the help of your teammates. This includes training models for customers and running evaluations as part of validating your research. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to explore ambitious ideas and ship the ones that work. Key Responsibilities Research and experiment with new model architectures to improve quality, efficiency, or capability Explore methods to decrease inference latency and improve serving efficiency Run experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques Perform reinforcement learning research to improve model alignment and capability Develop and improve our distillation pipeline for training high-quality models from frontier teachers Train models for clients and run evaluations to validate research findings in production settings Create robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance Stay current with ML research and identify techniques that can improve our platform Collaborate with applied engineers to bring successful research into production systems Document findings and share knowledge with the team Requirements 3+ years of experience training AI models using PyTorch Deep understanding of transformer architectures, attention mechanisms, and model internals Hands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar) Strong experimental methodology, including ability to design, run, and analyze rigorous experiments Track record of implementing ideas from recent ML papers Experience training on NVIDIA GPUs at scale Strong foundation in ML fundamentals: optimization, loss functions, regularization, generalization Nice-to-Have Publications in ML venues Experience with model distillation or knowledge transfer Experience with LLM speed optimization techniques Familiarity with vision encoders, multimodal models, or other modalities Experience with distributed training and infrastructure at scale Contributions to open-source ML projects You don't need to tick every box. Curiosity and the ability to learn quickly matter more. Compensation We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $250,000 - $350,000, plus equity and benefits, depending on experience. Equal Opportunity Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status. If you're excited about pushing the boundaries of custom AI research, we'd love to hear from you. Please send your resume and GitHub to amar@inference.net and/or here on Ashby.

About Lightfield Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context: accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. We’re rethinking CRM from first principles. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth. We’re building the CRM platform we always wished existed: fast, intelligent, and genuinely helpful. We are backed by Greylock, Lightspeed, and Coatue, and our founders previously built Tome, a generative AI presentation product used by over 25 million people. Before Lightfield, our team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce. About the role Lightfield's AI/ML team builds the experiences at the core of our product, developing new applications to wow our customers. Today, the team is focused on building a powerful, domain-specific AI that outperforms generic LLMs We’re inspired by the challenge of creating innovative new AI products for people doing serious work, and we’re looking to grow our AI/ML team to meet that challenge. What you'll do Create and ship magical, highly-differentiated AI experiences that sales teams can’t live without Craft Lightfield AI/ML strategy in tight collaboration with founders and execs Pioneer the training of new models that leverage both historical data and synthetic training data Prototype innovative, LLM-powered experiences, and drive their development into robust product features Help build a world class AI/ML engineering team by recruiting and mentoring teammates Who you are You have 5+ years of industry experience in NLP, with a strong portfolio of model training You have a strong understanding of deep learning AI/ML frameworks or cloud services You have hands-on ML Ops experience You possess deep expertise in NLP and model training, specifically with LLMs Proven experience adapting open-source generative models for specific use cases, demonstrating a deep understanding of its architecture and capabilities Bonus Points Proven track record of leading successful AI/ML research projects in a product environment Publications in applied AI/ML scientific journals Experience navigating open source/vendor solutions in LLM ops space (Langchain, Llama, Pinecone, etc) Benefits & Perks Competitive salary Meaningful early equity Health insurance (medical, dental, vision) 3 weeks of PTO 11 paid company holidays + we enjoy a winter holiday break 3 months of paid family leave Wednesdays work from home Regular team dinners, events, offsites, and retreats 401k plan Other perks include: commuter and lunch stipend

The Bot Company We're building a helpful robot for every home. We're a small team of engineers, designers, and operators based in San Francisco. Our team comes from Tesla, Cruise, OpenAI, Google, Pixar, and many other great companies. In the past we've shipped to hundreds of millions of users and know what it takes to build amazing products and experiences. Our team is deliberately lean to promote rapid decision making and do away with bureaucracy and hierarchy. Everyone is an IC and is empowered with massive scope, radical ownership, and direct responsibility. We work across the stack with a culture built for rapid iteration and fast execution. What we look for in all candidates All roles at The Bot Company demand extreme sharpness and the ability to move fast in high-intensity environments. Throughout the process, we expect candidates to demonstrate: Exceptional mental acuity: you think quickly, learn instantly, and reason across unfamiliar domains. Engineering curiosity: you naturally dig into how systems work, even outside your specialty. High performance mindset: you move fast, handle ambiguity, and excel when the environment is demanding. Machine Learning: Whole-Body Control We are building high-performance whole-body controllers that produce robust, agile motion and manipulation in the real world. You will train low-level control policies in simulation and own the stack from environment design to large-scale training and sim-to-real deployment. What You'll Do Train whole-body policies for locomotion, manipulation, and coordinated motion. Build scalable simulation environments in IsaacLab, MuJoCo, or similar with parallel rollouts. Design rewards and curricula that enable stable long-horizon learning. Own sim-to-real transfer using domain randomization and structured evaluation. Run and debug large-scale GPU training experiments. Requirements Very strong coding skills in Python, C++, or Rust. CUDA is a plus. Strong foundations in modern reinforcement learning. Experience training embodied control policies. Hands-on robotics simulation experience. Solid intuition for dynamics, contact, and actuation. Comfortable running large-scale GPU experiments. Why Join You’ll work with a small, elite team on challenges that require speed, intelligence, and deep engineering instinct. If you enjoy understanding systems at all levels, move fast, and think even faster, you’ll thrive here.

About Alljoined Alljoined aims to solve the communication bottleneck between humans and technology by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale EEG datasets collected on affordable hardware to decode images, text, and video initially, and eventually moving to internal thought. We are state-of-the art in capabilities and are fully vertically integrated. Our goal is to develop a general consumer interface to completely transform what we can do at home and work. We are actively growing our world-class team of researchers to build the next interface to improve individual lives as well as the well-being of society as a whole. About the Role We’re seeking a talented Machine Learning Researcher to join our core R&D team. This role involves designing and implementing advanced machine learning models for EEG-based neural decoding, publishing high-impact research, and developing the core infrastructure for our brain decoding systems. You will work closely with leading experts in neural decoding and AI, pushing the boundaries of what’s possible in brain computer interfaces. Key Responsibilities Research & Model Development: Develop, train, and refine state-of-the-art deep learning models for neural decoding, building on the latest advancements in ML architectures (e.g., transformers, diffusion models, etc). Explore novel approaches for modeling high-frequency timeseries EEG datasets along with a number of adjacent data modalities. Translate research insights into production-grade code that integrates seamlessly with our in-house BCI stack. Collaboration & Publication: Collaborate with a team of neuroscientists and ML engineers to create scalable, end-to-end neural decoding solutions. Publish findings at top-tier ML and AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR) and contribute to open-source communities where appropriate. Qualifications Educational Background & Experience: Bachelor’s degree in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Mathematics, Biomedical Engineering, etc), with 5-7 years of experience in ML research or applied ML engineering; OR Graduate degree (M.S., Ph.D.) in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Biomedical Engineering) with 3+ years of experience in ML research or applied ML engineering. Candidates with a Ph.D. and/or experience in high profile ML research labs are strongly preferred. Technical Expertise: Multimodal Representation Learning (CLIP-style contrastive objectives, masked autoencoding) Generative Modeling (diffusion, transformer-decoders, latent-GANs) Temporal Sequence Modeling (state-space models, STFT-aware transformers, RWKV) A track record of high-quality research demonstrated by publications in top ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR). Strong proficiency in Python and PyTorch, familiarity with ML tooling, and distributed training. Experience working in a production-quality codebase with modern code review standards. Compensation Range $140,000 - $250,000/year + equity While this represents our expected range based on market data, final compensation will be determined based on your specific qualifications and may be outside this range. Benefits Options for housing support Visa sponsorship 3% 401k matching Health insurance

About Us: At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most. Job Description: Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence. You’ll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures. Key Responsibilities: Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking. Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments. Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer. Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them. Optimize models and inference workflows for deployment on edge compute hardware. Document model architecture, training methodology, dataset provenance, and validation results. Qualifications (Preferred): Master's or PhD in Machine Learning, Signal Processing, or a closely related field — or equivalent demonstrated experience. 5+ years building and deploying ML systems with a focus on RF or signals data. Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus. Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data. Proven ability to ship production models, not just research prototypes. Experience in maritime, aerospace, or operationally demanding spectral environments. Experience building labeled RF datasets from ground truth sources. Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation). Active Secret clearance or demonstrated ability to obtain one.

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Machine Learning Engineer, Assessments

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 We’re hiring an ML Engineer, Assessments to help build best-in-class assessment systems across multiple products (Speak for Business, B2C, and new surfaces). You will work in a tight loop with our Assessment Design Lead (Content/Learning Design) , Machine Learning, Product, and Engineering to turn assessment constructs and rubrics into reliable, scalable scoring + feedback systems. This role owns the implementation, deployment, and ongoing quality of our assessment algorithms and ML systems. While there is immediate need to improve and expand production assessments, this work is also building a platform capability that can be reused across the app. What you’ll be doing Ship and own assessment ML systems end-to-end Build, deploy, and maintain scoring models/pipelines (feature extraction → model training → inference → feedback generation) Own monitoring, regression tests, and ongoing iteration to maintain accuracy targets Define and operationalize evaluation Implement validation/evaluation frameworks for assessments, including metrics, test sets, and offline/online analysis Translate assessment requirements into measurable acceptance criteria and guardrails Partner deeply with the Assessment Design Lead Co-develop the strategy, together with the Content team, to grow assessments into a core platform at Speak Work in a tight weekly loop to deliver incremental improvement Drive near-term delivery across products Stand up or improve summative assessments (spoken language ability) and bring them reliably to production Prototype and validate formative assessment approaches to measure improvement over weeks/months Support data and labeling strategy Help define data needs for training/evaluation (including psychometric measurement needs) Build or improve pipelines that support label collection and analysis (especially for efficacy studies) What we’re looking for Domain expertise in spoken language proficiency assessment (linguistics, applied linguistics, pedagogy, or equivalent experience) Strong experience designing and running evaluation + validation for assessment/scoring systems, and tailoring approaches to a specific product use case 4+ years building automatic proficiency assessment systems (or equivalent depth in closely related scoring/evaluation domains) PhD is helpful but not required Proven ability to ship ML models to production (not only research), including reliability, monitoring, and iteration Strong generalist ML/analysis skills (statistics, Python, PyTorch/model training) Ability to operate cross-functionally and communicate clearly with non-technical partners (Content/LD, PM, leadership) Nice to have Experience with speech/audio ML Experience with psychometrics concepts (reliability/validity, calibration) How we work (collaboration expectations) This role is designed to be highly collaborative with the Assessment Design Lead. Success depends on a tight loop where constructs/rubrics and model outputs co-evolve — not a sequential handoff. Why work at Speak 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. 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. 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, and Tokyo, and you’ll have the opportunity to talk to users in each of these regions on a regular basis as well as travel. 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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Senior Machine Learning Engineer

Negotiable

Senior Machine Learning Engineer Remote US & Canada We are seeking a highly motivated and autonomous Senior Machine Learning Engineer to join our team. In this role, you will be a driving force behind our core ML models and underlying data and training pipelines. We are looking for a self-starter, someone who is internally driven, passionate about their craft, and capable of collaboratively identifying, prioritizing, and executing workflows with their peers and stakeholders. If you thrive in an environment where you are trusted to own your projects end-to-end and can seamlessly explain complex technical concepts to both technical and non-technical stakeholders, we want to hear from you. We expect you to: Have deep passion for video games and think on behalf of our players Be curious, a problem solver and a self starter who is comfortable taking risks and proactively identifying and pursuing business needs. Know how and when to apply your knowledge, and be willing to share it with those around you Be comfortable with ambiguity and able to navigate it to iteratively refine the problem space Be responsible for your domain of expertise on projects from inception to delivery and beyond Required Skills 5+ years of experience in Machine Learning, Data Engineering, or a related quantitative field, with a track record of delivering end-to-end ML products Expert knowledge of statistical modeling and ML methods including Classical ML, Deep Learning, NLP, and Anomaly Detection, with the ability to select the right tools for the task Demonstrated proficiency in experimental design, hypothesis testing, and evaluating model performance Proven track record of building and deploying scalable ML solutions in production, ideally using Kubernetes and GCP Strong proficiency in Python, Java, or Scala, with an emphasis on writing clean, maintainable, and production-ready code, alongside the ability to write highly optimized SQL for complex dataset construction Mastery of optimized SQL and experience with big data processing frameworks (e.g., Spark, Beam) and modern data lakehouse formats (e.g., BigQuery, Snowflake, Iceberg, Parquet) Exceptional ability to translate complex technical architectures and ML concepts for non-technical stakeholders Preferred Experience Any of the following would be highly preferred, but most of all, we value team players who are eager to learn and contribute: • Experience with graph databases, collaborative filtering, or user vectorization • Experience with large-scale sentiment analysis • Experience with Typescript or Golang • Experience with source control, CICD, Protobuf, or infrastructure as code • Experience in the video game industry • Experience in a small company with a startup feel • Interest in technical writing and sharing your work Perks: • Paid Time Off, Holidays and Two Weeks Winter Break • Employees and their dependents get medical, dental, and vision coverage, regardless of their level, tenure, or position within the company. Moreover, these benefits start on the first day of the job—there’s no waiting period before they kick in. • Pet Insurance for those who need it too. • Compassionate leave for employees who needs to take care of their family members • Pre-tax wellness stipend • Pre-tax work from home stipend • Access our savings plan (401K program) with company match • Mental health resources including Headspace membership and Employee Assistance Program (EAP) • Discount portal for everyday goods and services • Employee inclusive and diversity initiatives such as Grow Together • Support for personal professional development We look forward to meeting you! Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. The salary range for this position is $120,000 USD to $205,700 USD annually, with the opportunity to earn an annual discretionary bonus. This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. Employees in this position are eligible to participate in the Company’s standard employee benefit programs, which currently include the following: medical, dental, vision, 401k, and paid time off. Our base pay is benchmarked against regional market and industry data and is adjusted to reflect the cost of living in your specific geographic area. LI-Remote

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ML Infra Engineer (TPU/Jax/Optimization)

Negotiable

In this role you will help scale and optimize our training systems and core model code. You’ll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines. You’ll work closely with researchers and model engineers to translate ideas into experiments—and those experiments into production training runs. This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure. The Team The ML Infrastructure team supports and accelerates PI’s core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs. In This Role You Will - Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging. - Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction. - Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization. - Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments. - Manage compute resources: Ensure efficient allocation and utilization of cloud-based GPU/TPU compute while controlling cost. - Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale. - Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities, and evaluation metrics. What We Hope You’ll Bring - Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms. - Hands-on large-scale training experience in JAX (preferred), PyTorch. - Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines. - Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS). - Ability to debug and optimize performance bottlenecks across the training stack. - Strong cross-functional communication and ownership mindset. Bonus Points If You Have - Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels). - Experience operating close to hardware (GPU/TPU performance tuning). - Background in robotics, multimodal models, or large-scale foundation models. - Experience designing abstractions that balance researcher flexibility with system reliability.

About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We’re looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you’ll be responsible for building and operating the core systems that power large-scale ML training and inference across TensorWave’s GPU platform , working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact. What You’ll Do Design, operate, and improve ML infrastructure systems supporting distributed training and inference workloads Build reliable, repeatable workload execution and orchestration patterns across shared GPU environments Troubleshoot performance, reliability, and scalability issues across the ML stack Partner with ML, systems, and platform teams to improve developer experience and operational efficiency Who You Are Required Qualifications Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience Expertise supporting production ML systems using SLURM and Kubernetes Strong understanding of GPU-accelerated workloads and distributed systems concepts Solid Linux fundamentals and experience debugging infrastructure-level issues Ability to build automation and tooling - Python, Go, etc. Preferred Qualifications Experience working across schedulers, orchestration platforms, or cluster managers Familiarity with large-scale GPU environments or HPC-style systems Experience improving infrastructure reliability, utilization, or performance at scale What We Offer Stock Options 100% paid Medical, Dental, and Vision insurance for Employees Company Health Savings Account Contributions 100% paid Short Term and Long Term Disability Insurance for Employees Life and Voluntary Supplemental Insurance Options Other Insurance Options, such as Pet & Legal Insurance Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support Flexible Spending Account 401(k) Employee Assistance Program Flexible PTO Paid Holidays Parental Leave Other In-Office Perks Equal Employment Opportunity TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law. Reasonable Accommodations TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact accomodations@tensorwave.com. Employment Eligibility All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law. Background Checks Where permitted by law, employment may be contingent upon the successful completion of a job-related background check. Data Privacy Notice By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.

Senior/Staff Machine Learning Data Scientist In Brief We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives. TLDR : Independent end-to-end model development with ability to work cross-functionally with Clinical, Engineering, and Product to clarify and prioritize specifications and features for our life-saving AI models. Who We Are Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data. We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year. Read more about our recent publication in Nature Medicine that associates our products with lives saved. What you’ll do As a Senior/Staff Machine Learning Data Scientist, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code. Responsibilities Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods Productionizing: The same models that you develop with production-grade Python Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploy production-grade Python code to implement those strategies Cross-Functional Alignment : Data Science for storytelling – understand model performance and metrics, and present this to technical and non-technical users, both internally and externally Minimum qualifications Ph.D. in a relevant field plus 3+ years experience shipping ML based software products Experience owning your ML models from prototyping to production, especially real-time algorithms that update dynamically across time Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems Track record of using statistics and performance metrics to compare end-to-end ML and product performance Preferred qualifications Experience shipping breakthrough or 0-1 products from end to end, interpreting and leveraging State-of-the-Art methods to do so Experience using messy clinical and health data to design new products for large Health Systems Experience with any of the following: PyTorch, PySpark, HL7, FHIR, EHR, time series data, signal processing, MLFlow, anomaly detection, Bayesian statistics, quantile regression, time-series forecasting You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives! Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to 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 laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

About Mindbeam We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. What drives us is not only advancing technology, but empowering the people behind it. We are a community of researchers, engineers, and visionaries who believe that collaboration, curiosity, and openness fuel progress. If you’re motivated by impact and inspired to build tools that others can build upon, you’ll be in the right place. Mission Design and optimize large-scale pre-training systems that power Mindbeam’s generative AI models. Role Expectations • Build scalable pre-training pipelines for foundation models, optimizing throughput and efficiency. • Implement distributed training strategies across GPUs/TPUs and high-performance clusters. • Collaborate with researchers to translate experimental setups into production-ready workflows. • Develop monitoring and fault-tolerance systems to ensure reliable large-scale training. • Continuously benchmark and tune performance across hardware and software stacks. Background • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience. • 2+ years of experience with large-scale model training and distributed systems. • Strong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX). • Experience with GPU scheduling, memory optimization, and parallelism strategies. • Comfort with containerized and orchestrated environments (Docker/Kubernetes). • Understanding of high-performance computing and networking bottlenecks. About You You thrive on scale and complexity. You enjoy solving system-level bottlenecks, pushing hardware and software to their limits, and working closely with researchers to accelerate cutting-edge AI development.

About Mindbeam We are building the next-generation AI infrastructure for both open-source and enterprise applications. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. Mission Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale. Role Expectations • Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression. • Implement scalable systems for model deployment, monitoring, and optimization. • Collaborate with researchers to validate experimental results in production contexts. • Build tools to automate benchmarking and regression testing. • Identify opportunities to improve efficiency in resource utilization and inference speed. Background • Bachelor’s, Master’s, or PhD in Computer Science, ML/AI, or related field—or equivalent practical experience. • 2+ years of experience in model training, evaluation, or deployment. • Strong skills in Python, ML frameworks (PyTorch/TensorFlow), and data pipeline tools. • Familiarity with optimization techniques (quantization, pruning, distillation). • Hands-on experience deploying models on cloud and/or GPU infrastructure. • Knowledge of monitoring and observability tools. About You You combine deep technical expertise with a pragmatic mindset. You thrive on bridging research and production, and you’re motivated by the challenge of making cutting-edge models usable and efficient at scale.

About Mindbeam We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. Mission Push the boundaries of performance by developing custom kernels and low-level optimizations for next-generation AI workloads. Role Expectations • Design and implement custom GPU/accelerator kernels to maximize performance. • Profile, benchmark, and optimize critical ML workloads. • Collaborate with researchers to translate algorithmic advances into efficient, production-ready code. • Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design. • Document and share best practices for low-level optimization. Background • Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience. • 2+ years of experience in GPU programming, parallel computing, or systems-level optimization. • Strong coding skills in C++, CUDA, or similar languages. • Familiarity with ML frameworks and their low-level backends. • Experience optimizing workloads for distributed and heterogeneous compute environments. • Comfort with profiling tools and performance diagnostics. About You You are detail-oriented, performance-obsessed, and excited by the challenge of squeezing out every ounce of compute efficiency. You enjoy working at the intersection of algorithms and hardware, and you thrive in a collaborative environment where bold ideas are encouraged.

About FINNY FINNY is a growth platform for financial advisors. We are on a mission to make great financial advice easier to find. Today, access to quality financial guidance is limited, not because advisors don’t exist, but because the right connections are hard to make when it matters. We’re fixing that with AI-powered tools that help advisors find, engage, and retain the clients they can genuinely help. We've raised a $4.5M Seed from Y-Combinator in S24 and now $17M Series A led by Venrock. We work with over 1,000 firms across the wealth management ecosystem, and have been recognized as the leader in fintech innovation—winning #1 at the Morningstar Fintech Showcase, #1 at 2025 Wealthies, and being featured across the industry. We’re based in Chelsea, NYC, building fast and ambitious systems at the intersection of data, AI, and real-world wealth services. About the Team Being a Machine Learning Engineer at FINNY means owning the models that power search, matching, ranking, recommendations, data and intelligent automation across the product. This is a model first role. While you’ll work with real production data and pipelines, your primary impact comes from designing, training, evaluating, and improving ML systems that directly shape user outcomes. You’ll partner closely with product, backend, and frontend teams to turn ambiguous problems into measurable model improvements. What You’ll Do Help build FINNY’s core models Design, train, and iterate on custom models that power data imputation, prospect and audience recommendations, campaign customization and personalization, and automations. Build & improve models in production Take models from research → experimentation → deployment → iteration Own offline evaluation, online metrics, and feedback loops Improve model performance over time through better objectives, features, and training strategies—not just more data Advanced modeling & experimentation Apply and adapt techniques such as: Fine-tuning RL methods (DPO) Transfer learning and weak supervision Synthetic data generation and augmentation Operate effectively in low-signal, noisy, or cold-start environments Contribute to ML systems & infrastructure Work with backend engineers to productionize models reliably and at scale Help define standards for model versioning, evaluation, deployment, and monitoring Influence long-term ML strategy and reduce technical debt in modeling workflows What We’re Looking For You’re a model builder at heart You care deeply about how models learn, not just how pipelines run You’re comfortable reasoning about loss functions, tradeoffs, and evaluation You enjoy designing solutions when the problem is underspecified and data is imperfect You’re strong technically Very strong Python with extensive hands-on experience building ML systems. Strong statistical and mathematical foundations. Proven experience training, fine-tuning, and deploying custom models into production, not just experimentation or offline research Experience designing loss functions, evaluation metrics, and validation strategies aligned with real-world product objectives Familiarity with model lifecycle management: versioning, reproducibility, monitoring, and iteration in production environments You’ve shipped ML systems before You’ve taken models beyond notebooks and into real products You understand failure modes, monitoring, and iteration in production ML Startup experience Your working style You tackle ambiguity head-on and turn fuzzy problems into concrete experiments You move fast, iterate, and aren’t precious about first approaches You communicate clearly about model behavior, limitations, and tradeoffs In-person, NYC (5 days/week in Chelsea office) Compensation & Benefits FINNY offers a competitive compensation package including: Competitive salary and equity Medical, dental, and vision insurance Flexible paid time off 401(k) Food and meals provided in our NYC office Team offsites and events Equal Opportunity Employer FINNY is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

About the Role We're looking for founding Machine Learning Engineers (MLEs) to own and improve our core action models end-to-end - the intelligence that powers Composite's proactive automation platform. You'll work at the intersection of LLM inference, browser understanding, and low-latency systems, shipping models that need to feel instant while reasoning over complex page state and user context. Unlike hosted browser solutions that introduce latency and auth barriers, or consumer-focused "AI browsers," we run AI directly through professionals' existing browsers via a Chrome extension, creating instant response times with zero migration or IT friction. This architecture creates unique ML challenges. This is a high-ownership role on our small, exceptional team where your work ships directly to users and has the potential to tangibly improve the work lives of hundreds of millions of people. About Composite College-educated professionals spend 85% of their day as digital factory workers in Chrome, clicking through repetitive browser tasks. Composite is building the proactive layer for productivity so professionals around the world can focus on meaningful, high-leverage work. We're training action prediction models that run in real time, anticipating what you'll do next based on page context and prior interactions. We've raised $5.6M in seed funding led by Nat Friedman and Daniel Gross, with participation from Menlo Ventures, Anthropic's Anthology Fund, SVAngel, and other incredible investors. What You'll Work On Improve the accuracy and latency of our core models across diverse web applications to predict users' intended next actions and execute them faster than manual input Design and optimize LLM inference pipelines, including token caching strategies, streaming architectures, and network-level optimizations between client and server Build evaluation frameworks and data pipelines to measure and improve model quality at scale Experiment with retrieval-augmented approaches using vector databases for contextual memory Develop synthetic data generation pipelines for browser interaction training data Work with DOM states, accessibility trees, and user interaction data to improve browser understanding Ship features end-to-end that go directly to users — this is not a research-only role What We're Looking For ML & Systems Strong ML fundamentals with hands-on experience training and deploying models in production Obsessive about latency — experience optimizing inference pipelines to feel instant to end users Deep care about data quality, with the instinct to build tooling that ensures it Experience with LLMs, transformer architectures, or sequence prediction problems Comfortable working across the stack — our system spans a Chrome extension, Electron app, Cloudflare Workers edge proxy, and inference providers Core Qualities Character: You're someone we'd want to work closely with for the next ten years. You approach challenges with curiosity rather than ego. You're a team player, a great communicator, and aren't afraid to be wrong. Work Ethic: You're energized by hard problems and comfortable working intensely toward ambitious goals. Raw Intelligence: You can quickly understand complex systems and solve novel, ambiguous problems with self-guidance. Bonus Experience with browser automation, Chrome extensions, or web scraping at scale Familiarity with accessibility tree / DOM parsing for page understanding Background in RL or online learning from user interaction data Experience with vector databases (e.g., Turbopuffer, Pinecone) and hybrid search Full-stack development experience (TypeScript, Node.js, React) Our Values Disagree and commit: Respectfully challenge decisions you disagree with, even when it's uncomfortable. Don't censor yourself or your ideas. Once a decision is determined, everyone commits wholly. Clear and consistent standards: Decisions are made based on a shared framework that applies for everyone. We don't leave room for "rules for you, not for me" or any perceived hypocrisy. Over-communicate: Nothing slows down a company more than confusion, mis-, or under-communication. Leave no room for ambiguity. Ask dumb questions. Write things down clearly. Health is #1: Stay hydrated. Eat a balanced diet. Sleep 8 hours a night. Exercise frequently. Maintain good social and mental health. Not doing so affects your mood and long-term productivity. Do the right thing.

Weave is looking for engineers hungry for fun challenges who can join our self-empowered teams and contribute in both technical and non-technical ways. You will be joining a team of talented developers that share a common interest in distributed backend systems, data, scalability, and continued development. You will get a chance to apply these, and other skills, to new and ongoing projects to make machine learning more approachable, data more available, and easier to discover and use by helping design how teams build out AI powered features at Weave. Our teams are cross-functional agile teams composed of a product owner, backend and frontend devs and devops. Teams are highly autonomous with the ownership and ability to act in Weave’s best interest. Above all, your work will impact the way our customers experience Weave while working closely with a highly skilled team to accomplish varying goals and cultivate our phenomenal culture. PURPOSE The Machine Learning Team's mission is to enable product innovation by making it painless for developers to build ai powered applications that require access to large sets of data. Machine learning is challenging but we are striving to democratize access to the tools and technology that powers it so teams can build cutting edge features safely and responsibly without a PhD in Data Science. As a Machine Learning Engineer on the team you’ll be building models for new products with emerging technologies, at scale. We handle data for hundreds of millions of people daily. This position will be available for fully remote in the US with an opportunity to work in an office, if located near the Lehi, UT Headquarters. Reports to: Engineering Director What You Will Own Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences. Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning. Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products. Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end. Build scalable, resilient services to support data integration, event processing, and platform extensions. Contribute to the continued evolution of product functionality that services large amounts of data and traffic. Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce. Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices. Work in a cloud environment, considering the implementation of functionality through several distributed components and services. Work with our stakeholders to translate product goals into actionable engineering plans. What You Will Need to Accomplish the Job High integrity, team-focused approach, and collaboration skills to build tight-knit relationships across Weave with various roles and stakeholders. Responsive person with a strong bias for action. 5+ years of experience in any structured back-end language, i.e. Go, Java or Python (Go and Python experience is a plus). Experience moving and storing TBs of data or 100M’s to 10B’s of records. Experience building and deploying ML driven B2B multi-tenant applications in production environments. Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow, etc), DVC, Triton Server, LLMs, Postgres, and others. Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, multi-modal models, and others. Experience with data labelling or annotation for audio or text use cases. Understanding of distributed systems and building scalable, redundant, and observable services. Expertise in designing and architecting systems for distributed data sets and services. Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.). Experience providing stable well designed libraries and SDKs for internal use. Self driven and a thirst for learning in a quickly changing industry. Demonstrated track record of delivering complex projects on time and have experience working in enterprise-grade production environments. Strategic thinker with a strong technical aptitude and a passion for execution. What Will Make Us Love You A background with data analysis, visualization, and presentation. 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience. Experience with natural language models, embeddings, and inference in production, at scale. Experience with real-time audio models and voice use cases such as transcription, ASR pipelines with interruption detection, audio alignment, and speech synthesis. Experience with emerging technologies such as Model Context Protocol (MCP). Proficient understanding of containers, orchestrators, and usage patterns at scale including networking, storage, service meshes, and multi-cluster communication. Experience with Kubernetes or GKE and the Operator Pattern (GCP), specifically, a plus. Experience with highly sensitive data such as PHI (HIPAA) and PII data. Experience with automation and container based workflow engines. Experience with GitOps, IaC, and configuration driven systems. A preference for open source solutions. A track record of clean abstractions and simple to use APIs. Deep understanding of distributed data technologies such as streaming, data mesh, data lakes, warehouses, or distributed machine learning. A desire to advance the state of the art with new and innovative technologies. Enjoys working in a greenfield environment using rapid prototyping. Enjoys working with open-ended, evolving problems, and domains. LI-DNI Employment with Weave is contingent upon the successful completion of a background check , conducted in accordance with applicable laws. At Weave, we use Artificial Intelligence (AI) tools to help us work more efficiently and create a smoother candidate experience. AI may assist with things like writing job descriptions, scheduling interviews, or reviewing applications against job-related criteria. For additional information, please review the External AI Policy Statement available on our Careers page. Weave is an equal opportunity employer that is committed to fostering an inclusive workplace where all individuals are valued and supported. We welcome anyone who is hungry to learn, problem-solve and progress regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other applicable legally protected characteristics. If you have a disability or special need that requires accommodation, please let us know. Beware of recruitment fraud . All official correspondence will occur through Weave branded email. We will never ask you to share bank account information, cash a check from us, or purchase software or equipment as part of your interview or hiring process.

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Machine Learning Research Engineer (MLRE) - Workfl...

Negotiable

Why Achira At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed. At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success. About the Role We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models. While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities. What You’ll Do Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack. Rationalize machine learning systems design and software architecture. Identify blockers and build solutions that scale to the size of foundation models. Operate as the glue between research scientists and the infrastructure team. About You At least two years relevant industry experience. Highly fluent in and enthusiastic about PyTorch and JAX. Used to thinking in asynchronous primitives. Strong views on library design: clean abstractions, minimal surface area, consistency. Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code. ML generalist who knows what scalable, reliable ML systems look like. Nice to Have Even if you hit none of these bonus features, we encourage you to apply! Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery. Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc. Lack of fear around interacting with quantum chemical scientists and their data pipelines.

About Air Space Intelligence ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible. What You Will Do: As part of our Defense engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale. What We Value: Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain Strong understanding of data structures, algorithms, and software engineering best practices. Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts. Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools. Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines. A bias for simplicity and clarity in solving complex problems. Intellectual curiosity and willingness to collaborate. Clear communication and collaboration across cross-functional teams. How We Hire: We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you. ASI works with export controlled technology and restricted U.S. Government data, including on contracts mandating U.S. immigration status and location restrictions for performing personnel. Employment offers are contingent on ability to timely obtain all required authorizations for contemplated job duties.