Machine Learning Jobs in Santa Clara, CA

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Leads the architecture and development of FPGA compiler and physical implementation technologies. Designs optimization algorithms for timing, area, power, routability, scalability, and runtime; integrates innovations into production tool flows; investigates customer implementation challenges; and provides technical leadership and mentorship. Collaborates with engineering, product management, and strategic customers while contributing to patents, publications, and the long-term roadmap of Altera’s FPGA software platform.

Lead the multi-year strategy, architecture, roadmap, lifecycle governance, and market expansion of the FlashBlade hardware platform. Partner with executives, engineering, supply chain, and customers to define high-performance storage solutions for AI and HPC workloads. Evaluate market dynamics, platform economics, supply constraints, performance, cost, and reliability trade-offs while integrating emerging networking, fabric, and media technologies. This primarily in-office role is based in Santa Clara, California.

Lead the architecture and strategy of autonomous vehicle evaluation systems. Develop metrics, statistical and machine-learning methods, automated workflows, dashboards, and release-gating criteria to assess AV performance, identify regressions, and support safety decisions. Collaborate with autonomy, simulation, systems, safety, and data teams across the development lifecycle. Analyze large-scale time-series data, investigate anomalies, influence technical strategy, and provide system-level insights for validated autonomous driving deployment.

Own end-to-end AI data verticals, building scalable machine learning and backend distributed systems while driving product strategy, revenue, customer growth, and market expansion. Partner with strategic customers, solve complex production ML challenges, and lead applied AI initiatives involving evaluation, physical AI, multimodal systems, and LLMs. The role requires founder-minded technical and business ownership in an ambiguous, high-growth environment.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Lead launch and sustaining product marketing for large language and world models. Develop positioning, messaging, go-to-market plans, technical content, campaigns, and adoption programs. Translate AI research, benchmarks, architectures, and capabilities into accessible stories for researchers, developers, enterprises, partners, and media. Coordinate cross-functional launches, support communications and executive visibility, track competitive developments, and measure awareness, engagement, and adoption.

Designs scalable AI, data, and technology solutions for finance by translating CFO organization needs into technical specifications and architectures. Partners with finance, IT, data, security, product, and engineering teams to address data flows, automation, ERP, compliance, and security requirements. Leads solution design, identifies technology gaps, guides project and workstream execution, and co-develops partner solutions that solve measurable business problems.

Leads Cisco CX AI engineering strategy and teams developing production-grade generative and agentic AI solutions. Responsibilities include architecting AI platforms, scaling data-driven systems, managing software and ML engineers, standardizing AI tools and infrastructure, partnering with product and business stakeholders, and establishing responsible AI, context engineering, and observability practices.

Develops technical product marketing and go-to-market strategy for Cisco Wireless solutions. Creates positioning, messaging, technical content, demonstrations, thought leadership, web content, and sales enablement materials. Collaborates with product, engineering, sales, customer, and marketing teams to drive customer understanding and demand. Represents Cisco Wireless at industry events and webinars while serving as a technical and market authority for enterprise wireless networking.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.

Design, develop, test, document, and debug complex software for semiconductor equipment components and devices. Define software specifications and test plans, troubleshoot difficult issues, implement bug fixes, and communicate with customers on requirements, schedules, and technical problems. The role requires expertise in C/C++, .NET, C#, operating systems, networking, algorithms, multithreading, and RPC, with opportunities to lead small projects.

Simulate complex hard disk drive servo-mechanical systems using finite element analysis, dynamics, electromagnetics, vibro-acoustics, and computational fluid dynamics. Validate models through correlation experiments, collaborate with mechanical and servo design engineers to optimize products, and analyze manufacturing tooling and processes for robustness. This early-career role includes structured training, mentorship, and development support.

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Lead full-cycle recruiting for XPENG’s U.S. HR team, primarily supporting Mexico, Brazil, and broader Latin America. Responsibilities include talent mapping, sourcing, screening, interview coordination, offer negotiation, stakeholder advising, pipeline analytics, employer branding, and candidate experience. The role requires strong LATAM market knowledge, cross-cultural collaboration, executive communication, and the ability to manage multiple searches onsite in Silicon Valley. Recruiting expertise in AI, machine learning, autonomous driving, robotics, or software engineering is preferred.

Design, implement, and evaluate machine learning and deep learning algorithms for autonomy, trajectory prediction, and perception. Develop predictive and decision-making models, conduct experiments in simulated or dynamic environments, and build scalable research tools. Collaborate on research prototypes involving agent behavior, environmental understanding, and generative AI. Communicate findings to leadership and help transition validated research systems into production.

Develop autonomy algorithms for semantic understanding, edge-case detection, data mining, and causal modeling of autonomous vehicle behavior. Design onboard systems, manage sensor dependencies, optimize large evaluation datasets, and define performance metrics. Collaborate with platform, product, and security engineering teams to deploy machine learning techniques into production.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Leads systems software architecture, roadmap development, and technical execution for complex hardware-software products. Designs modular solutions, derives requirements, integrates hardware and software, improves code quality and development efficiency, and supports continuous integration, testing, troubleshooting, and legacy modernization. Coaches junior architects and collaborates with multidisciplinary teams on manufacturability, serviceability, and testability.