Top AI & Machine Learning Jobs

Lead end-to-end implementation of an AI orchestration platform: scope engagements, write code, architect integrations with customer tooling (Figma, Slack, Teams, Adobe, dashboards), run deployments, drive adoption and measurable outcomes, and manage a team of implementation engineers and solution architects while coordinating across platform, account, and customer stakeholders.

Senior individual contributor leading AI adoption in firmware engineering: evaluate AI tech, prototype and scale LLM/agent systems, build AI-assisted CI/CD and IDE tooling, architect secure AI infrastructure, and drive upskilling and cross-functional partnerships to improve firmware quality and velocity.

Design, train, and deploy perception, SLAM, prediction, and motion-planning ML systems for safe navigation. Translate research into production C++/Python implementations, build data pipelines and evaluation infrastructure from fleet logs, optimize on-device performance, own end-to-end features from sim to fleet rollout, and mentor engineers across robotics, ML, and systems.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Own network cost unit economics by building and optimizing plan configuration models, validating them against carrier invoices and financial data, forecasting usage and spend, and supporting pricing and contract decisions. Move recommendations into automated, auditable workflows, maintain dbt and Redshift data models, investigate discrepancies, and communicate quantified business impacts.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

Build coupled fusion-device simulation models, radiation and surrogate models, diagnostic data pipelines, Bayesian inference systems, and uncertainty quantification tools. The role covers pulsed-power circuits, plasma dynamics, neutron and X-ray production, machine lifetime modeling, and HPC-enabled computation. The scientist will write production-grade numerical software, validate models against experimental diagnostics, document assumptions, and eventually grow a simulation team.

The AI Developer will develop, test, and deliver NICE CXone AI solutions, focusing on chat bots and other AI systems, while also supporting production and development teams.