Machine Learning Jobs in Sunnyvale, CA

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.

Design and build large-scale machine learning systems for search ranking, semantic retrieval, personalization, and product discovery. Develop scalable offline and real-time ML pipelines, process billions of interaction records and petabytes of logs, deploy complex models, track performance metrics, and collaborate with product, engineering, and data science teams. The role also involves mentoring engineers and applying transformers, LLMs, embeddings, vector search, and multimodal learning.

Build scalable performance tooling for AI model training and inference across cloud and embedded hardware. Responsibilities include profiling workloads, conducting roofline analysis, identifying layer- and operator-level bottlenecks, predicting latency, memory, utilization, and compute costs, and monitoring regressions. The role requires close collaboration with model, compiler, runtime, platform, and hardware teams to establish performance targets and translate measurements into actionable recommendations.

Own and extend Wayve’s machine learning compilation pipeline from model checkpoint to deployable embedded bundles across NVIDIA TensorRT and Qualcomm QNN targets. Design compiler passes, quantization and precision-typing systems, graph partitioning, legalization, regression testing, and benchmarking infrastructure. Partner with model and training teams to ensure compilability, accuracy, and latency across architectures and SoCs. Provide technical direction and mentorship for compiler engineering.

As a Research Scientist Intern, you will support the technical team in developing AI product pipelines, assist with machine learning models, writing research papers, and analyze data for model improvement.

As a Research Scientist and Engineer at Collinear, you will create AI simulation environments, develop evaluation systems, and enhance real-world model performance through rigorous data-driven approaches.

Lead Collinear's research direction across evaluation, simulation, and post-training ML methods while managing a high-caliber research team. Influence customer outcomes through applied research.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

Lead the Trucking Autonomy team to develop behavior systems for L4 autonomous trucks. Oversee strategy, architecture, and team management.

The Research Engineer will develop AI algorithms for autonomous systems, collaborating with teams on end-to-end solutions and data-driven models.

The Technical Lead Manager will own the perception model for autonomous systems, leading a team to architect, train, evaluate, and deploy this model across diverse environments while reducing dependencies on traditional mapping technology.