Machine Learning Jobs in Santa Clara, CA

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Supports product strategy, requirements, and roadmaps for AI-powered features. Partners with engineering and design teams to translate customer insights into actionable requirements and data-backed development plans. The internship includes product research, competitive analysis, strategic planning, roadmap development, cross-functional collaboration, and exposure to product management methodologies.

Leads engineering and machine learning teams building enterprise-scale AI Search, retrieval, ranking, recommendation, personalization, and RAG systems. Responsibilities include setting architecture and strategy, deploying production ML models, improving relevance and performance, driving reliability and observability, partnering with product and engineering stakeholders, and mentoring technical teams delivering AI-powered experiences to millions of users.

Leads NVIDIA’s embedded GPU and long-life board product portfolio, managing customer relationships, design wins, pricing, supply commitments, lifecycle planning, and product roadmaps. The role coordinates Engineering, Sales, Finance, Operations, campaign teams, and partners through product introduction, launch, long-life support, and end-of-life transitions. It requires advanced hardware and software platform experience, commercial acumen, lifecycle management expertise, strong communication, and at least 25% travel.

Lead the architecture, integration, development, and verification of embedded vision software for next-generation surgical robotics. Responsibilities include integrating cameras, image sensors, FPGA/ISP pipelines, and multi-processor systems; debugging end-to-end imaging issues; developing simulation and diagnostic tools; managing illumination controls and risk analyses; supporting regulated medical-device development; and mentoring vision engineers.

Design, develop, test, document, troubleshoot, and debug complex software for semiconductor equipment components and devices. Define software specifications and test plans for simulators, test benches, and laboratory environments. Analyze requirements, implement bug fixes, communicate with internal and external customers, and contribute to machine learning and AI-driven software solutions.

Leads complex robotics, computer vision, machine learning, and software deployment programs across engineering, product, and field operations. Owns schedules, milestones, release processes, technical risk management, data-collection cycles, OTA infrastructure, and cross-functional alignment. Establishes metrics, dashboards, tools, and feedback loops to improve model development and field deployment reliability. Requires strong systems thinking, technical communication, and the ability to manage dependencies without direct authority.

Design and scale silicon spin-qubit processors from PDK development through tapeout, fabrication, validation, and system integration. Responsibilities include qubit standard-cell design, automated placement and routing, signal integrity and thermal analysis, customized EDA capabilities, PDK collateral, design-rule validation, interposer interfaces, and electrical characterization structures. The engineer collaborates with quantum, control, packaging, process, lithography, software, and external customer teams to enable large-scale quantum processor designs.

Verify ASIC inference accelerator designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertions, and UVM. Develop verification infrastructure, debug designs, optimize automated flows, and collaborate with architects, designers, and pre- and post-silicon teams. Apply knowledge of ASIC design flow, power verification, DFT, netlist verification, and machine learning techniques to improve verification processes.

Supports Kinaxis Demand and Supply Planning platforms, including configuration, forecasting workflows, supply-demand balancing, integrations, troubleshooting, data governance, and process optimization. Partners with planners and IT to gather requirements, document processes, monitor forecast performance, and implement statistical and ML-based planning improvements across global operations.

Lead technical recruiting for software and machine learning engineering teams. Develop hiring strategies, partner with engineering leadership, manage structured interviews, improve recruiting systems, analyze funnel metrics, mentor recruiters, and deliver strong candidate experiences. The role requires recruiting specialized technical talent, including autonomy, robotics, defense, embedded systems, or Physical AI professionals, in a fast-growing, in-office environment.

Build and manage technical talent pipelines across autonomy, AI/ML, embedded systems, and software engineering. Develop research-driven sourcing and talent-mapping strategies, partner with hiring managers to define candidate profiles, collaborate with recruiters to support candidate experience, and use market research and data to improve sourcing efforts.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Leads advanced device development for Intel Foundry semiconductor process technologies. Manages device engineers, directs experiments and electrical or physical data analysis, develops test structures, validates design rules, supports PDK delivery, and collaborates across integration, modeling, reliability, and foundry engineering teams. Uses statistical analysis, data mining, machine learning, and fab data to guide process development and ensure performance, yield, reliability, and high-volume manufacturing readiness.

Source technical talent for autonomy, ML/AI, embedded systems, and software engineering roles. Build and manage candidate pipelines, develop research-driven sourcing and talent-mapping strategies, partner with hiring managers, collaborate with recruiters, and use market research and data to improve sourcing approaches. The role requires strong communication, organization, relationship-building, and technical recruiting experience.

Leads the technical strategy, architecture, and roadmap for scalable fraud detection platforms. Develops reusable rules, machine learning, graph analytics, risk-scoring, APIs, and detection infrastructure for customer use. Partners across engineering, data science, security, risk, product, and customer teams to translate fraud patterns into production capabilities. Oversees detection metrics, root-cause analysis, system resilience, real-time processing, and adaptive feedback loops while managing complex technical initiatives and engineering organizations.

Design, implement, and optimize large-scale autonomous vehicle simulation testing pipelines and shared libraries. Build automation for test creation, data collection/analysis, debugging, and monitoring. Integrate AI tools to automate test generation and analytics, lead technical roadmaps, perform code reviews, and mentor engineers to improve AV behavior validation and deployment.

Define product strategy, requirements, and roadmaps for AI-powered features. Partner with engineering and design teams to translate customer insights into actionable, data-backed product decisions and roadmaps. The internship includes collaboration with cross-functional teams, targeted learning, networking, and exposure to product development within Cisco’s technology organization.

Develop and execute recruiting strategies for software and ML engineers, partnering with engineering leaders on hiring plans and talent strategy. Lead structured interviews, improve recruiting systems and processes, provide data-driven funnel insights, manage complex searches, mentor recruiters, and deliver an excellent candidate experience. The role focuses on scaling specialized technical teams in a fast-growing physical AI company.

Owns enterprise data architecture, standards, semantic layers, certified data products, and data governance across large-scale transformation programs. Leads SAP S/4 data transformation, Databricks integration, migration strategy, architecture reviews, vendor assessments, and technical delivery direction. Establishes data quality, security, access-control, and product operating models while influencing executives and cross-functional teams without direct reporting authority. Mentors architects and engineers and guides multi-year platform and technology investments.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Design and build scalable, reliable ML training infrastructure for advanced AI research and model development. Optimize distributed training performance, resource utilization, data loading, and costs across heterogeneous GPU and cloud environments. Improve platform observability, debuggability, operational excellence, and user experience. Collaborate with ML engineers, research scientists, and cross-functional partners to integrate new technologies supporting intelligent driving systems.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Lead cross-functional compiler software programs from concept to delivery, coordinating global teams, tracking risks and schedules, and improving development processes. Present status to executives, drive Agile and Waterfall projects, resolve technical/resource conflicts, and implement process and tool automation to ensure timely product releases.

Conduct foundational research in networking and system architecture for large-scale distributed AI and GPU systems. Design network architectures, algorithms, routing, congestion-control, scheduling, topology, and telemetry-driven control mechanisms, including AI/ML-based approaches. Build prototypes and simulations, publish research, develop patents, collaborate with academia, and define technology directions for future products. The role requires a relevant PhD, strong systems and networking research, programming and prototyping skills, and experience with AI/ML methods for systems.