Machine Learning Jobs in Sunnyvale, CA

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Lead editorial vision and content strategy for a technical AI research lab. Develop the editorial voice, calendar, content engine, long-form and short-form content, multimedia storytelling, and owned channels. Translate complex research into credible, accessible communications for technical and general audiences. Partner with engineers, researchers, communications, and product teams on launches and flagship reports; manage a social media manager, build a freelance writing team, and track content performance metrics.

Lead technical program management for NVIDIA GPU software, coordinating chipset and product roadmaps with software capacity. Manage cross-functional dependencies, prioritization, planning gaps, hardware and release-planning partnerships, process improvements, and execution metrics. The role requires delivering software products while balancing short-term critical timelines with long-term improvements to planning, processes, and team capabilities.

Lead the architecture and development of advanced analytics, data products, predictive models, anomaly detection systems, and GenAI solutions using Engineering and Supply Chain data. Translate stakeholder needs into scalable technical solutions, manage analytics and AI roadmaps, collaborate on production deployments, and integrate solutions into business processes. Ensure data quality, governance, security, explainability, bias detection, and responsible AI practices while influencing cross-functional stakeholders and driving adoption.

Analyze emerging AI workloads and build analytical performance models and architecture simulators for current and future AI inference accelerator hardware. Collaborate with hardware, compiler, inference server, kernel, and product teams to validate assumptions, identify HW/SW optimization opportunities, incorporate architecture research, and document methodologies. The role is a hands-on technical contributor focused on performance analysis across the hardware/software boundary.

Design, evaluate, and productionize agentic AI systems and LLM-driven components to improve agent behavior, reliability, latency, and user-facing experiences. Build eval benchmarks, tune models, manage data quality, and collaborate with platform teams to deploy scalable, safe, and high-performing AI features.

Design and productionize agentic AI systems and components, improve agent behavior and reliability, create eval benchmarks and high-quality datasets, tune and evaluate LLMs and models, optimize latency and reliability in production, and partner with platform and UI teams to deliver end-to-end ML solutions.

Build and productionize agentic AI systems: improve agent behavior, reliability, latency, and quality using LLMs, multimodal models, and vector databases. Design eval benchmarks, manage training and evaluation datasets, implement context engineering, orchestration, multilingual and multimodal support, and partner with platform teams to deliver production-grade, tested ML-driven features.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Build and operate enterprise AI integration products, including MCP servers, agent-facing APIs, React and TypeScript experiences, semantic retrieval systems, and automated AI quality evaluations. Improve latency, reliability, token efficiency, observability, and security across distributed systems. Lead cross-functional engineering initiatives, mentor engineers, and implement enterprise safeguards such as tenant isolation, audit logging, OAuth, and prompt-injection defenses.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Lead the strategy and development of next-generation post-training systems for Waymo’s autonomous driving AI. Advance post-training techniques, research applications for real-world autonomous driving, build scalable metrics, data, and flywheels, and collaborate with ML platform and onboard software teams. The role also requires influencing engineering strategy and stakeholders across the organization, including director and VP-level leaders.

Full-time hybrid summer technology internship at Johnson & Johnson from May 24 through August 13, 2027. Interns contribute to technology projects across software engineering, data engineering, AI and machine learning, cybersecurity, cloud and DevOps, UX, digital health, platform engineering, and connected devices. Responsibilities include building solutions, analyzing data and processes, collaborating with stakeholders, supporting project portfolios, and developing technology capabilities. The program may serve as a feeder into the Technology Leadership Development Program.

Design and implement machine learning algorithms for autonomous robotics using Python. Develop deep neural networks for end-to-end learning, 3D scene understanding, object detection, world reconstruction, and self-supervised learning. Build datasets from camera, LiDAR, and radar data; manage automated AI workflows; and support production machine learning pipelines from curation through validation. Collaborate across engineering teams and document solutions clearly.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

Lead research and development of auction mechanism design for online advertising. Evaluate and improve ranking/pricing algorithms, run large-scale experiments (A/B), build data pipelines and production software, apply ML/RL and optimization, and collaborate cross-functionally to deliver measurable business impact.

Lead end-to-end product development for SaMD neurodiagnostics (EEG) products. Define vision, write requirements and acceptance criteria, manage backlog and releases, integrate AI features, ensure FDA and global regulatory compliance, drive VOC and competitive analysis, and coordinate cross-functional execution from design through verification and launch.

Lead customer success for advanced analytics-based SaaS deployments, initially managing strategic enterprise accounts and growing into leading a Customer Success team. Responsibilities include driving customer adoption, planning and tracking complex cross-functional rollouts, presenting success metrics, supporting retention, identifying expansion opportunities, collaborating with Sales, and translating customer requirements into successful implementations.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Leads end-to-end execution of complex software platform programs across cloud infrastructure, platform services, system integration, validation, release delivery, and operational support. Coordinates software, infrastructure, product, and operations teams; manages dependencies, risks, technical issues, schedules, metrics, and senior-leadership reporting. Ensures full-stack readiness and delivery of scalable, reliable software supporting AI workloads.