Machine Learning Jobs in Santa Clara, CA

Lead the architecture, implementation, integration, and optimization of enterprise business systems across finance, supply chain, billing, and customer platforms. Develop system roadmaps, standardize tooling, automate workflows using middleware and AI, ensure SOX and ITGC compliance, troubleshoot complex issues, build reporting dashboards, and mentor team members. The role requires close collaboration with stakeholders and senior leadership to deliver scalable enterprise solutions.

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.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core, including vector and matrix engines. Own functional features, performance, power, area, timing, and physical design considerations. Develop specifications, optimize RTL, support verification, and collaborate on CPU modeling, design verification, and SoC integration. The role requires deep expertise in high-performance CPU architecture, cache and memory subsystems, SystemVerilog or related HDL technologies, and advanced processor design.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve low-latency and high-correctness problems, develop and adopt engineering frameworks, and create production-ready technical roadmaps. Partner with Product, Science, and Operations while mentoring engineers and improving engineering standards. The role requires experience with ML models, foundational models or LLMs, MLOps, Kubernetes, data pipelines, distributed workflows, and large-scale scheduling.

Develop autonomy algorithms and foundation models for semantic understanding of real-world driving data. Architect scalable machine learning systems, optimize sensor datasets through processing and auto-labeling, and deploy ML techniques into production. The role requires collaboration across platform, product, security, and engineering teams while solving complex autonomous-driving edge cases and maintaining high technical standards.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Develop machine learning algorithms and foundation models for autonomous driving data, including semantic understanding, edge-case detection, causal modeling, scalable ML systems, sensor-data processing, dataset optimization, and auto-labeling. Collaborate with platform, product, and security engineering teams to deploy ML solutions into production, including safety-critical physical systems.

Develop machine learning algorithms and foundation models for autonomous vehicle data, including scene understanding, causal modeling, dataset optimization, sensor-data processing, and auto-labeling. Design scalable ML systems and collaborate with platform, product, and security engineering teams to deploy models into production, including safety-critical physical systems.

Architect, productionize, and scale autonomous generative AI support agents. Lead agentic planning, memory, retrieval, reasoning, policy enforcement, safety, evaluation, multilingual capabilities, and cost optimization. Partner with Product, Design, and Operations to deliver reliable customer-facing automation at global scale. Mentor multiple engineering pods, establish technical strategy, and guide experimentation, rollout, and reliability standards.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

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 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.

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.

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 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.

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 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.

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.

Develop high-performance planning modules for autonomous vehicles, collaborate with customers, and apply modern software practices in a team-oriented environment.