Staff Software Engineer, ML Infrastructure
Design and build distributed training platforms for LLM and multimodal fine-tuning and post-training at scale. Implement and integrate state-of-the-art training algorithms into production pipelines. Own inference architecture and multi-provider routing, including failover and optimization. Research and implement inference optimizations including quantization, speculative decoding, and batching strategies. Lead initiatives to improve latency and cost efficiency across the training and serving stack. Build evaluation and experimentation infrastructure that enables rapid, reliable iteration. Drive technical direction, mentor engineers, and establish best practices for ML infrastructure.
ML Research Scientist (Health & Sensing)
As an ML Research Scientist at Eight Sleep, responsibilities include using AI and Machine Learning to transform sensor data into personalized intelligent health and fitness experiences. You will work closely with a cross-functional R&D and production team to prototype and ship solutions that improve sleep and health. Specific projects involve advancing the Pod's adaptive thermoregulation system using reinforcement learning and closed-loop control, developing multimodal health foundation models integrating physiology and environmental context from various data sources, and building high-fidelity physiological simulators to model the impact of daily behaviors on sleep and readiness. The role requires applying machine learning techniques to health-related problems and data to deliver impactful products for users.
Senior AI Platform Engineer (Autonomous Driving)
Set technical strategy and oversee development of a high scale, reliable data platform to manage, visualize, and serve large-scale datasets for ML model training and validation. Build the data lakehouse for autonomous driving scene datasets, including sensor data, calibration data, and annotation data. Drive the development of the Autonomous Driving Data SDK, including scene data search, datasets preparation, and dataset loading. Identify and resolve performance bottlenecks in data processing pipelines, including data processing latency, data search latency, and Test Procedure coverage. Bootstrap and maintain infrastructure for Data Platform components such as Data Processing Pipeline, Database, Data Lakehouse, and Data Serving. Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infrastructure teams, to align ML Platforms with the overall Autonomous Driving System Architecture.
Senior Product Manager – Platform
Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment, leveraging Snorkel Flow or designing custom approaches. Develop and implement state-of-the-art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Forge and manage relationships with customers’ leadership and stakeholders to ensure successful AI project development and deployment with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to align customer needs with capabilities, prioritize roadmap gaps, and guide successful project setups. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities and AI approach strengths and weaknesses. Serve as the voice of customers for new AI paradigms and workflows, sharing feedback with product teams. Conduct enablement workshops to transfer knowledge to customers using or considering Snorkel AI. The role includes up to 25% annual travel.
Researcher, Automated Red Teaming
This role leads the Automated Red Teaming (ART) effort, building scalable, research-driven systems that continuously discover failure modes in the models and mitigations, and translate those findings into actionable, production-facing improvements aimed at maximizing counterfactual reduction in expected harm by identifying high-leverage, least-covered weaknesses early and reliably. The researcher will own the research and technical direction for automated red teaming across catastrophic risk areas, initially focusing on automated classifier jailbreak discovery (cyber and bio), automated bio threat-development elicitation (worst-feasible planning uplift), and chain-of-thought monitoring evasion probing and related loss-of-control evaluations. The person in this role will partner closely with vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and implement mitigations; with the Classifiers team to convert discovered attacks into training data, evaluations, and measurable robustness improvements; and with product, engineering, and safety stakeholders to ensure ART outputs are operationally useful, not just theoretically interesting.
AI/Machine Learning Engineer Intern
As an AI/Machine Learning Engineering Intern, you will contribute to building intelligent product experiences that help students discover and secure opportunities. Your work will span search, recommendations, matching, and other discovery systems that power job exploration on Handshake. You will partner with senior engineers and data scientists to develop machine learning models that improve user experience, build Agentic pipelines/workflows to improve the Handshake student/employer user experience, contribute to experimentation, model evaluation, and performance monitoring. Additionally, you will participate in technical discussions, brainstorming sessions, and team reviews, and document methodologies and findings to support knowledge sharing and long-term system improvements.
Forward Deployed AI Engineer
The Forward Deployed AI Engineer is responsible for driving the end-to-end technical deployment of Latent Labs' models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure. The role involves designing and building production-grade API integrations, data pipelines, and model-serving infrastructure tailored to each customer's requirements. The engineer works on-site or embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues, and deliver solutions while ensuring deployments meet enterprise standards for security, performance, and reliability. Additionally, the engineer serves as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including on-site presence at international partner locations as needed. They gather and synthesize customer feedback, translating it into actionable insights for product, research, and platform teams, collaborating internally to shape the product roadmap based on deployment learnings. The responsibilities also include creating technical documentation, integration guides, and best-practice resources for customers. Moreover, the engineer is expected to engage in self-development activities such as staying current on ML infrastructure, model serving, and cloud-native tooling, gaining a working understanding of relevant biology, and participating in knowledge sharing within the team.
Member of Technical Staff, Applied AI
Develop a deep working understanding of the company's generative models including their architectures, training data, capabilities, and limitations. Collaborate in a joint codebase with other research scientists, engineers, and protein designers while maintaining the highest code standards. Lead the end-to-end technical deployment of the models into customer environments by designing production-grade API integrations and model-serving infrastructure. Adapt and fine-tune models to meet specific customer requirements, working closely with the research team to ensure scientific rigor. Build ML data pipelines for customer-specific inference, evaluation, and feedback workflows. Ensure deployments meet customer standards for security, performance, and reliability. Work embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues, and deliver solutions. Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams. Plan and carry out model inference against biological targets with customer biology teams, learn from results, and feed insights back into models. Gather and synthesize customer feedback and translate it into actionable insights for product, research, and platform teams. Create technical documentation, integration guides, and best-practice resources. Spend time working on-site at international partner locations as needed. Stay current with the latest developments in ML, model serving, and cloud-native tooling. Gain a strong working understanding of protein and cell biology. Participate in knowledge sharing including organizing and presenting internal reading groups and presenting at conferences.
Senior ML Operations (MLOps) Engineer
As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.
Software Engineer, Enterprise Zone (Backend & Full Stack) — Multiple Levels
The core responsibilities of the Software Engineer, Enterprise Zone role include leading responses to critical incidents and customer-impacting events, working directly with enterprise customers, collaborating across engineering, product, and support teams to solve urgent problems, building internal tools and improving core product features based on real-world customer feedback. Additionally, responsibilities include building a new extensible platform for account and organization-level settings with declarative controls and AI guardrail controls, designing and implementing scalable backend architecture and APIs for asset cataloguing, ownership, and security, building unified interfaces for organizing and governing assets across accounts and products, embedding AI technology to monitor and optimize account management, driving technical alignment with senior leaders, delivering solutions and proofs of concept balancing short-term execution with long-term vision, tackling data standards and compliance automation challenges, building platforms like User Notifications, Audit Logs, Public APIs, supporting compliance and error tracking use cases, improving observability features such as SLOs and dashboards, and leading projects from start to finish while utilizing AI to enhance development and customer experience. These roles involve working cross-functionally, impacting premium plan sales, collaborating with multiple teams, and playing a key role in the company's growth with close connection to customers daily.
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