Machine Learning Jobs in San Jose, 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.

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.

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.

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.

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.

Own lab infrastructure operations, including system bring-up, equipment deployment, platform validation, configuration, maintenance, automation, and troubleshooting. Support server, GPU, FPGA, AI, memory, and environmental test platforms while enabling workload characterization, trace analysis, performance testing, and customer activities. Develop Python or shell automation, manage assets and calibration, and resolve hardware, firmware, software, networking, power, and thermal issues across global engineering teams.

The role involves developing LLM applications for automated silicon design, optimizing models, curating datasets, and collaborating with other experts to integrate LLMs into EDA workflows.

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.

Design, build, and operate production-grade agentic AI systems for ServiceNow. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, RAG and search pipelines, safety guardrails, observability, model integration, evaluation frameworks, and cost and latency optimization. The role also provides technical leadership through architecture decisions, code reviews, and coaching.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.