Machine Learning Jobs in San Jose, CA

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.

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.

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.

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.

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, 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.

Leads data science strategy, standards, experimentation, metrics, and analytics delivery for eBay Live. Owns a major domain such as buyer or seller analytics, trust and safety, data foundations, or business performance. Establishes rigorous methodologies, oversees cross-functional programs, develops scalable measurement assets, guides risk modeling and experimentation, and communicates insights to executive stakeholders. Mentors analytics leaders, drives organizational alignment, and promotes AI and machine learning innovation.

Lead end-to-end data science and experimentation for eBay Live, covering product analytics, marketplace modeling, risk signals, instrumentation, dashboards, and business performance. Mentor data scientists, establish analytical quality standards, partner with engineering on scalable data solutions, apply AI/ML where appropriate, and present evidence-based recommendations to senior leaders.

Leads Cisco’s applied AI research organization, setting long-term research strategy across networking, security, programming, and human-computer interaction. Directs research teams, advances representation learning, structured prediction, generative models, and large language models, and translates prototypes into production enterprise solutions. Partners with product, engineering, and design teams, mentors researchers, and establishes ethical AI, privacy, security, and governance standards. Represents Cisco through publications, conferences, and external partnerships.

Develop and evaluate perception algorithms for autonomous vehicles, including road-user detection, scene understanding, and road-structure estimation. Train and optimize machine learning models, validate performance in simulation and real-world driving environments, analyze failure modes, and build research software in Python and C++. Collaborate with autonomous systems teams, present technical findings, and contribute to publications, patents, and research initiatives.

Lead legal counsel for AI and machine learning matters, advising engineering and product teams on intellectual property, regulatory compliance, contracts, privacy, open-source licensing, product design, and risk mitigation. Develop AI/ML legal playbooks, compliance processes, training, and IP protection strategies while collaborating with cross-functional stakeholders and presenting emerging risks to executives and the Board.

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

The role involves improving demand forecast accuracy, implementing ML models, and collaborating with various teams to analyze market trends.

Develop production-ready deep learning and image-processing algorithms for semiconductor metrology and inspection systems. Responsibilities include image-to-CAD alignment, non-rigid registration, defect detection and localization, zero- to low-shot training, algorithm robustness, and real-time processing optimization. The role collaborates with software engineering, architecture, and product teams to integrate scalable solutions. A relevant M.Sc. or Ph.D. and 10 years of deep learning and image-processing R&D experience are required.

Develop application architecture and scalable software for humanoid robots and autonomous machines. Integrate hardware, software, manipulation algorithms, and whole-body controllers; lead profiling, benchmarking, verification, and system performance improvements. Collaborate with internal engineering teams and external partners to deploy robotic solutions globally. The role requires hands-on robotics software development, machine learning and reinforcement learning expertise, simulation-based validation, and proficiency in Python and C/C++.

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Own and deliver a major subsystem of ServiceNow’s exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Lead architecture, design and code reviews, production delivery, metrics, AI safety guardrails, and technical direction. Partner with product, security R&D, and SecOps while mentoring engineers and guiding ambiguous AI/ML problems to scalable, reliable production systems.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.

Develops end-to-end data science and AI solutions for supply chain and service operations. Responsibilities include building machine learning, GenAI, LLM, agentic AI, RAG, forecasting, and deep learning applications; deploying and monitoring models in production; designing GenAI architectures; analyzing operational data; partnering with stakeholders to improve forecasts and business decisions; and communicating insights to leadership.