Machine Learning Jobs in Santa Clara, CA

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 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.

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.

Own the strategy, roadmap, and delivery of a unified Cyber Resilience platform across cloud, on-premises, and hybrid environments. Lead product convergence onto shared services, define technical capabilities, prioritize requirements, and partner with engineering, security, data, customers, and go-to-market teams. Drive migrations, launches, adoption, reliability, and measurable customer outcomes while making trade-offs across value, risk, resources, and technical dependencies.

Build scalable machine learning pipelines, data and training infrastructure, distributed processing workflows, and reusable software systems for foundation-model research. Own codebase health, testing, reliability, maintainability, and performance across ML workflows. Collaborate with researchers and engineers to translate model and experiment requirements into robust systems supporting large datasets, large models, multimodal data, video, robotics, and embodied AI.

Develop advanced autonomy algorithms for physical AI, including data mining, deep scene understanding, causal modeling of ego-vehicle behavior, and robust identification of complex driving edge cases. Deploy algorithmic solutions for real-world, safety-critical autonomous systems and help establish high technical standards within Uber’s AV Labs team.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Develop machine learning algorithms and scalable AI systems for autonomous driving. Responsibilities include end-to-end ML implementation, sensor dependency management, dataset collection and auto-labeling, system architecture, and production deployment in safety-critical environments. The role collaborates with platform, product, security, cloud, compute, and big data engineering teams while providing technical leadership.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Develop autonomy algorithms that extract semantic meaning from urban driving data, architect onboard systems, optimize evaluation datasets, and deploy machine learning techniques into production. The role involves managing sensor dependencies, defining performance metrics, identifying complex edge cases, and collaborating with platform, product, and security engineering teams to support autonomous vehicle data and safety-critical systems.

Develop autonomy algorithms that extract semantic meaning from complex driving scenarios and enrich Uber’s autonomous vehicle data lake. Design onboard systems, manage sensor dependencies, define evaluation datasets and metrics, and deploy machine learning techniques into production. Collaborate with platform, product, and security engineering teams. The role focuses on physical AI, deep scene understanding, causal modeling, and robust solutions for safety-critical autonomous driving systems.

Develop autonomy algorithms and foundation models that extract semantic insights from complex driving data. Design scalable machine learning systems, manage sensor dependencies, optimize datasets through data processing and auto-labeling, and deploy models into production. Collaborate with platform, product, and security engineering teams while providing technical leadership. The role requires expertise in Python, Linux, modern ML frameworks, and preferably autonomous driving, safety-critical systems, C++, and high-performance computing.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Develop autonomy algorithms that extract semantic meaning from complex urban driving scenarios and enrich AV data. Design onboard systems, manage sensor dependencies, define evaluation datasets and metrics, and collaborate with platform, product, and security engineering teams to deploy machine learning techniques into production.

Develop and optimize statistical, mathematical programming, and machine learning models for Uber’s dynamic pricing systems. Analyze marketplace data, design experimentation and causal inference methods, identify algorithm improvements, and deploy solutions with Product, Engineering, and Operations. Build diagnostic tools, logging infrastructure, and marketplace metrics while translating complex analyses into actionable strategies for business leadership.

Develop autonomy algorithms that add semantic understanding to real-world driving data. Design onboard systems, manage sensor dependencies, define evaluation metrics, and build scalable production solutions for safety-critical autonomous vehicle systems. Collaborate with platform, product, and security engineering teams to deploy onboard systems.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.

Designs, develops, tests, troubleshoots, and enhances large-scale distributed systems software. The role involves building networking, database, utility, and internet-related subsystems; integrating microservices and AI/ML services; debugging code; and creating testing documentation. The engineer leads project teams, collaborates with internal and outsourced development partners, communicates project progress, and mentors less-experienced engineers.

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++.