Machine Learning Jobs in Santa Clara, CA

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization. Collaborate with engineers, research scientists, technical program managers, and product managers to deliver responsible AI-powered products and shape the long-term roadmap for foundational AI platforms.

Owns end-to-end evaluation and validation processes for autonomous vehicle software and end-to-end machine learning stacks. Designs scalable metrics, ELT infrastructure, automated triage strategies, and KPI monitoring using simulation and on-road data. Performs root-cause and behavioral analysis, communicates data-driven recommendations, and connects continuous improvement efforts to product safety cases and compliance standards. Collaborates with AI/ML, simulation, systems engineering, and product teams.

Serves as a technical lead for customers using CoreWeave cloud infrastructure, with emphasis on Kubernetes in high-performance computing environments. Responsibilities include designing and deploying tailored solutions, leading proofs of concept, optimizing AI/ML workloads, conducting technical reviews, advising on product strategy, collaborating with engineering teams, and representing CoreWeave at industry events.

Lead Expedia Group’s company-wide AI evaluation framework, defining standards, metrics, governance, tooling, and adoption strategies for accuracy, safety, transparency, latency, and helpfulness. Partner with engineering, data science, product, and platform teams to build scalable evaluation workflows, publish insights and scorecards, influence AI investment and risk decisions, and establish responsible operating models for deploying trustworthy AI products.

Leads the architectural vision and delivery of AI-powered loyalty systems across distributed engineering teams. Designs scalable APIs, data models, cloud services, and frontend architectures; solves complex technical problems; mentors engineers; drives modernization, operational excellence, and cross-functional alignment across Product, Data Science, and platform teams.

Designs and implements evaluation, validation, testing, and simulation frameworks for autonomous vehicle software and ML systems. The role analyzes simulation and on-road data, develops performance metrics and confidence measures, performs root-cause analysis, automates regression detection, and communicates evidence-based insights to technical stakeholders. It requires strong Python skills, systems-engineering experience, analytical problem solving, and cross-functional collaboration.

Leads the strategy and execution of AI model performance and on-vehicle inference for autonomous driving. Responsibilities include managing engineering leaders, setting performance budgets, optimizing models and GPU systems, establishing benchmarking and profiling practices, detecting regressions, building performance dashboards, and guiding cross-functional architecture and production decisions. The role also oversees hiring, coaching, staffing, KPIs, technical leadership development, and trade-offs across latency, memory, throughput, accuracy, power, and numerical parity.

Develop and productionize machine learning systems for personalization, ranking, recommendations, retrieval, and adaptive customer experiences. Design experiments, evaluate model performance, engineer features, prepare data, and improve model quality. Collaborate with engineering, product, analytics, and science teams on scalable ML services, APIs, data models, deployment, monitoring, and operational performance. Apply deep learning, recommender systems, sequential modeling, embeddings, experimentation, and MLOps practices in large-scale consumer environments.

Designs and operates large-scale Kubernetes, hybrid-cloud, and data-center infrastructure; builds SRE tooling, Infrastructure-as-Code, GitOps pipelines, observability, SLOs, and automated runbooks. Develops AI- and machine-learning-driven incident detection and auto-remediation to reduce toil and MTTR. Leads reliability, disaster recovery, cost optimization, on-call operations, and incident management across global teams while mentoring engineers and driving DevOps adoption.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

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.

Lead AI Engineer responsible for designing, developing, deploying, and supporting scalable AI systems. The role focuses on foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, observability, and optimization. Responsibilities include improving AI system scalability, cost, latency, throughput, and hardware utilization; applying emerging research in production; and contributing to the long-term technical roadmap for foundational AI platforms.

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization. Collaborate with engineers, scientists, technical program managers, and product managers while shaping the technical vision and roadmap for responsible AI platforms.

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.

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.

Design, build, deploy, scale, and monitor production machine learning systems. Develop models, application code, data pipelines, cloud architectures, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams, ensure high availability and performance, govern models for risk and responsible AI, and lead teams developing ML solutions.

Lead data science for AI Foundations, developing and deploying large-scale NLP, machine learning, and generative AI solutions. Partner with software engineering, ML engineering, product, and design teams to build customer-facing applications. Train, fine-tune, evaluate, and operationalize language models and other advanced models using technologies including PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. Provide technical leadership, communicate complex findings, and develop team talent.

Lead the design, development, deployment, and support of scalable AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques to improve AI system scalability, latency, throughput, hardware utilization, and cost. Shape foundational AI roadmaps, collaborate with cross-functional teams, and mentor engineers while applying emerging research to production.

Supports data and analytics consulting projects through research, data analysis, visualization, predictive analytics, and machine learning tasks. Uses Python, SQL Server Data Tools, Power BI, Tableau, and Alteryx to manipulate data, validate findings, develop visualizations, and generate actionable insights. Collaborates with experienced professionals while learning consulting methodologies, artificial intelligence applications, and data-driven solution development.

Develop and productionize GenAI, LLM, machine learning, and analytics solutions for ASIC semiconductor engineering. Build agentic systems, RAG workflows, predictive models, and failure-analysis tools; deploy and monitor AI solutions in cloud and enterprise environments; collaborate with architecture, design, data science, and IT teams; and communicate technical findings while identifying opportunities to improve engineering productivity, quality, cost, and cycle time.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Leads internal audit and SOX engagements, manages client relationships, develops audit methodologies, evaluates financial statements and internal controls, conducts risk assessments, and oversees audit reporting. The role includes project ownership, budgeting, stakeholder management, team leadership, mentoring, process improvement, and use of AI and risk technology to enhance audit effectiveness and address compliance and governance risks.

Develop AI-driven automation and CAD methodologies for memory silicon design, including layout generation, floorplanning, AMS, PDN, analysis, optimization, and design-space exploration. Build production-quality software frameworks integrating EDA tools and workflows, improve design quality and productivity, and collaborate with design, layout, CAD, verification, and product teams to accelerate design convergence and time-to-tapeout.

Leads an engineering team building scalable ML data infrastructure, including data ingestion, processing, storage, access, pipelines, and analytical platforms. Defines architecture, evaluates technologies, improves performance and reliability, translates data requirements into technical solutions, and ensures governance, security, and regulatory compliance. Provides mentorship, strategic roadmap input, and cross-functional collaboration with ML engineers, data scientists, platform engineers, and product teams.

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.