Top AI & Machine Learning Jobs

Join the founding team to design and build research infrastructure, automate compute clusters and data pipelines, and develop software/APIs enabling access to generative AI systems for protein therapeutics. Internship with Bay Area location (remote for exceptional candidates).

Build software connecting Fabri’s digital manufacturing process, including manufacturing data collection, process analytics, workflow automation, machine integration, backend services, and internal tools. Develop and deploy machine-learning models for outcome prediction, anomaly detection, inspection, and process monitoring. Analyze process data and geometry, improve manufacturing quality and throughput, and collaborate directly with operators, engineers, and manufacturing teams in an on-site factory environment.

Leads enterprise strategy and implementation of agentic AI systems, including autonomous workflows, copilots, and decision-support agents. Builds and scales teams developing agent frameworks, orchestration, integrations, and governance. Owns the lifecycle from use-case design through deployment, monitoring, and improvement while establishing reliability, evaluation, safety, auditability, and value standards. Partners cross-functionally to embed AI responsibly into business workflows and translates complex AI capabilities into business outcomes.

Build net-new AI products for high-stakes decision simulations while partnering with principals as a core product owner. Develop novel data-gathering techniques using voice and analysis agents, and post-train models to improve automated data procurement and analysis pipelines. The role combines product ownership, software engineering, and research in an intense, in-person San Francisco environment.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Owns end-to-end product surfaces and production AI features, including LLM applications, prompt and evaluation pipelines, retrieval, agent workflows, reliability, and full-stack web and mobile development. The role requires substantial ownership, production AI experience, launch resilience, and technical leadership in a fast-paced, in-person environment.

Lead end-to-end data science projects involving causal inference, experiments, statistical modeling, machine learning, political behavior, and online narratives. Build scalable data pipelines and production-quality Python code from messy datasets, rigorously evaluate models, collaborate with researchers and engineers, and contribute methods and technical direction. Mentor junior modelers while translating ambiguous questions into practical, deployable solutions.

Build and scale AI-powered marketing systems, including creative and landing-page generation, performance measurement, data integrations, and agentic workflows. Develop MCP servers and connect Claude with Snowflake, Braze, advertising platforms, GA4, and web systems. Own conversion optimization, attribution analysis, creator-program automation, experimentation, compliance guardrails, and multi-agent growth infrastructure. The role combines production software engineering, marketing automation, analytics, and rapid experimentation in a highly autonomous builder environment.

Research Scientist developing robot autonomy software and learning algorithms for dexterous manipulation in unstructured environments. Responsibilities include reinforcement and imitation learning, high-performance inference, production robot data pipelines, distributed policy training, edge deployment, and safety-critical software. The role also advances robotics research through novel methodologies while applying results to customer use cases.

Design, develop, and maintain scalable backend systems for a composite AI platform (agent-based systems, LLM integrations, RAG). Own technical vision and roadmap, make architecture decisions, establish engineering best practices, provide senior technical leadership, and deliver high-impact production software.