AI Jobs in San Francisco

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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As a Researcher for cybersecurity risks, you will design and implement mitigation components for model-enabled cybersecurity misuse that span prevention, monitoring, detection, and enforcement, under the guidance of senior technical and risk leadership. You will integrate safeguards across product surfaces in partnership with product and engineering teams to ensure protections are consistent, low-latency, and scalable with usage and new model capabilities. Additionally, you will evaluate technical trade-offs within the cybersecurity risk domain, propose pragmatic and testable solutions, and collaborate with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and misuse scenarios. You are expected to execute rigorous testing and red-teaming workflows to stress-test the mitigation stack against evolving threats across different product surfaces and iterate based on the findings.

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As a Lead Engineer at Eloquent AI, you will lead the development of AI-powered full-stack applications while overseeing and mentoring other engineers. You will remain hands-on across the stack, take ownership of technical direction, code quality, and delivery standards. Responsibilities include designing and building full-stack applications that power AI-driven workflows for enterprise users, overseeing and reviewing the work of other engineers to ensure high-quality, production-ready code, providing technical guidance, architectural direction, and hands-on support where needed, developing high-performance front-end interfaces for AI agent control, monitoring, and visualization, building scalable backend services that support real-time AI interactions, knowledge retrieval, and automation, working closely with AI researchers and ML engineers to integrate LLMs, RAG, and automation into production-ready systems, establishing engineering best practices across testing, deployment, and performance optimisation, and continuously iterating and refining AI-driven products balancing speed with robustness.