Machine Learning Jobs in Santa Clara, CA

Build and maintain scalable backend search services for Uber Eats, Grocery, and Retail. Develop lexical, semantic, ML-powered, and LLM-based retrieval and ranking systems, indexing infrastructure, and model-serving capabilities. Improve search relevance, latency, throughput, reliability, and observability while debugging production issues and collaborating with ML, data science, product, and engineering teams. Write well-tested production code and contribute to technical design decisions.

Lead software development in test for Roche Digital Pathology products, designing automated verification for image-processing algorithms, pattern recognition, and platform integrations. Develop scalable data-analysis tools, cloud-based testing and deployment pipelines, and reusable automation frameworks. Collaborate with imaging scientists and product teams to validate technical performance and scientific accuracy, troubleshoot failures, support requirements and design reviews, and execute formal verification. Mentor junior engineers and promote software engineering, testing, and release best practices.

Design and build AI-driven web products and platform infrastructure for chip design workflows. Implement high-performance APIs, backend services, data pipelines, and distributed systems. Own technical strategy, collaborate with product and engineering teams, and work across frontend and backend to deliver scalable, maintainable tools.

Own the strategy and roadmap for seller onboarding risk products on Walmart Marketplace. Build AI-powered onboarding, identity verification, fraud detection, and automated decisioning capabilities that balance seller growth, compliance, and customer trust. Partner with Engineering, Data Science, Design, Operations, and Compliance to define requirements, metrics, experiments, and scalable product solutions. Use data and customer insights to improve activation, conversion, risk detection, and seller experiences while responding to emerging fraud trends.

Leads GM’s autonomous vehicle evaluation organization, defining strategy, roadmaps, methodologies, infrastructure, metrics, risk assessment, and release-gating approaches. Oversees evaluation of perception, prediction, and planning systems using statistical and machine learning methods, scalable data platforms, workflows, and AI techniques. Builds and manages engineering teams, drives architectural decisions, establishes KPIs, aligns cross-functional stakeholders, and translates system-level results into decisions for engineering and executive leadership.

Leads the strategic vision, architecture, and execution of an AI-driven silicon design platform. Builds and manages engineering leadership teams, governs MLOps and model lifecycle practices, drives cloud and containerized platform scalability, establishes security standards, manages vendors and budgets, and represents the organization to executives and customers. The role oversees NLP, vector databases, knowledge graphs, machine learning pipelines, RAG, multimodal AI, and customer-facing software platforms.

Designs customer-centered digital experiences, functional prototypes, user flows, wireframes, journey maps, and interface specifications. Collaborates with product, engineering, business, and research teams to define, test, and deliver solutions across responsive web and mobile platforms. Applies human-centered design, user research, usability testing, AI-assisted workflows, and responsible AI interaction patterns while facilitating workshops and supporting product prioritization.

Own and grow a territory of 12–15 strategic enterprise accounts in the Greater Houston market. Develop new business, expand customer relationships, exceed sales targets, and sell networking solutions to senior technology executives. Build account plans, manage channel partnerships, conduct demand-generation events, collaborate with systems engineers and technology partners, and position solutions involving networking, SDN, cloud, data centers, and AI/ML.

Conduct applied machine learning and NLP research for eBay’s marketplace. Design, train, and evaluate language and multimodal models; analyze datasets; build data pipelines; validate outputs; and integrate research into scalable production systems. Collaborate with engineering and product teams, prototype novel methodologies, document findings, and monitor advances in LLMs and machine learning. A master’s degree in computer science, engineering, or a related field is required.

Conduct foundational research in autonomous vehicles, robotics, and physical AI. Design and implement new methods for perception, reasoning, closed-loop training, safety assurance, navigation, and human interaction. Publish original research, collaborate with internal and external researchers, open-source results, and transfer innovations to product teams. The role requires a 2026 PhD, relevant autonomy or robotics research experience, strong Python skills, machine learning framework experience, and research publications or comparable artifacts.

Leads a portfolio of complex supply chain programs, including product ramps, technology transitions, process transformations, and supplier readiness initiatives. Owns integrated planning, governance, risks, dependencies, budgets, executive decisions, dashboards, testing, cutover, adoption, and post-program learning. Develops program managers, aligns stakeholders across supply chain and corporate functions, drives organizational change, and applies AI, automation, and analytics to improve supply chain performance.

Develop and deploy AI agents and learning-based systems that optimize VLSI design workflows. Responsibilities include designing algorithms, integrating machine learning into software tools, analyzing design data, and collaborating with VLSI, machine learning, and software engineering experts. The role requires production-quality Python development, experience with AI agents or decision systems, and proficiency in PyTorch or TensorFlow, with LLM experience as a preferred qualification.

Design and implement intelligent automation for AV software development, testing, release, and operational workflows. Build CI/CD pipelines integrating simulation and hardware-in-loop testing, develop engineering data pipelines and metrics, and create AI-enabled tools for test analysis, failure classification, defect triage, reporting, and workflow orchestration. Establish evaluation, governance, security, and monitoring standards for AI solutions while improving release readiness, software quality, engineering productivity, and cross-functional visibility.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Build and operate a security data platform connecting SIEM, identity, asset, threat intelligence, and knowledge graph systems. Design core APIs, distributed data pipelines, schemas, risk scoring, entity resolution, and coverage queries. Own architecture and platform workstreams from scoping through delivery, collaborate with detection and identity teams, review code, mentor engineers, and drive partner adoption. The role requires expert Python, deep Splunk expertise, cybersecurity experience, and senior-level project ownership.

Develop and refine software-driven behaviors for humanoid robot demonstrations and external events. Responsibilities include behavior sequencing, control logic, hardware testing, debugging, event setup, rehearsals, live troubleshooting, and collaboration with engineering and operations teams. The role requires strong Python skills, AI or machine learning experience, robotics fundamentals, and hands-on hardware iteration under tight timelines.

Leads pre-sales architecture and consultative selling for AI, ML, HPC, and data management solutions. Designs scalable data architectures, AI workflows, generative AI applications, and performance-optimized deployments. Conducts discovery, demonstrations, presentations, proofs of concept, and technical enablement while supporting deal closure and customer adoption. Collaborates with sales, product, engineering, marketing, hyperscalers, OEMs, and channel partners to develop joint solutions and go-to-market strategies.

Supports machine learning platform infrastructure across distributed environments. Responsibilities include monitoring AI platform performance, optimizing training and inference workloads, planning GPU and cloud capacity, evaluating model deployment metrics, troubleshooting infrastructure issues, improving data pipeline and storage performance, and collaborating with engineering teams on scalable, efficient AI platforms.

Build, deploy, and operate production machine learning services for document extraction, visual understanding, and agentic automation. Responsibilities include integrating LLMs, designing APIs and pipelines, deploying scalable services on Kubernetes, implementing testing and observability, evaluating model quality, resolving production issues, and collaborating with product and engineering teams on end-to-end features.

Internship opportunities supporting General Intelligence and Robotics Centers. Work may involve foundation models, multimodal pretraining, reinforcement learning, autonomous driving, scalable AI infrastructure, inference systems, robotics, world models, embodied agents, manipulation, locomotion, and whole-body motion control.

Build, deploy, scale, and maintain machine learning models and infrastructure for production use. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software development practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Leads digital tool-enabled service-product development and new product introduction evaluations for semiconductor equipment. Integrates service requirements into product development, coordinates cross-functional technical and customer teams, develops predictive analytics and machine-learning capabilities, and ensures service readiness, reliability, documentation, training, and field support. Builds business cases, supports go-to-market strategies, analyzes customer and market needs, manages product changes, and communicates program status, risks, outcomes, and recovery plans.

Owns product-level yield, quality, qualification, and reliability for silicon photonics products through high-volume manufacturing. Defines yield methodologies, analyzes test and qualification data, leads cross-functional improvement initiatives, translates wafer and process changes into product outcomes, and maintains data analysis and traceability workflows. Partners with design, process, test, reliability, supply chain, foundry, and back-end teams to optimize PIC specifications and manufacturing performance.

Leads a global team of approximately 70 employees overseeing corporate IT infrastructure, end-user services, engineering labs, IAM, endpoint management, unified communications, service operations, and technical support. Develops infrastructure strategy, governance, automation, AI-enabled employee services, and scalable XaaS and on-premise solutions. Partners with Product Engineering to improve Zoom products, manages technical debt and vendors, applies security and compliance frameworks, and ensures reliable global support and system performance.