Machine Learning Jobs in San Jose, CA

Design and build cloud-based AI and machine learning solutions for ServiceNow’s conversational analytics products. Drive architecture and technical direction, create prototypes, collaborate with engineering and product teams, and own software through delivery. Develop agentic analytics capabilities involving semantic layers, knowledge graphs, data catalogs, and contextual engines. Coach engineers, communicate with technical and executive audiences, and prioritize customer outcomes such as accuracy and evaluation quality.

Develop and deploy ML and generative AI solutions to predict and optimize power delivery networks (PDN), automate PDN root-cause analysis, and enable AI-assisted power integrity and packaging co-design across design, CAD, and product teams to accelerate tapeout and improve silicon outcomes.

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.

Develop and productionize machine learning systems for personalization, recommendation, ranking, retrieval, traveler understanding, and sequential modeling. Lead end-to-end scientific work including problem formulation, data exploration, feature engineering, model development, experimentation, evaluation, and operationalization. Collaborate with engineering, product, and business stakeholders on system and API design, mentor other scientists, influence technical direction, and apply advanced deep learning and foundation-model techniques at scale.

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.

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

Own the design and adoption of ServiceNow’s AI product evaluation and feedback ecosystem. Build automated evaluations, human review processes, feedback taxonomies, and closed-loop improvement mechanisms across AI-powered support products. Partner with Product, AI/ML Engineering, Data, Telemetry, and Operations teams to define requirements, metrics, governance, and prioritization. Use evaluation findings and customer support insights to improve resolution quality, establish shared development standards, and influence senior stakeholders across the organization.

Develop and deploy EDA/CAD software, automation, and AI-assisted tools to accelerate custom memory IC design workflows. Collaborate with design, layout, verification, and CAD teams to integrate LLMs, generative AI, and automation into verification, modeling, and tape-out flows.

Identify enterprise AI opportunities, evaluate emerging models and platforms, build prototypes and proofs-of-concept, quantify business value, and present recommendations to technical and executive stakeholders to drive AI adoption across the company.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Lead and manage internal audit and SOX engagements, implement audit methodologies, use AI and risk technology to optimize processes, assess controls and financial statements, mentor staff, produce audit reports, and collaborate with stakeholders to mitigate risks and support client growth.

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.

Lead vision, roadmap, and commercial strategy for Expedia Group's Travel Media Network. Drive product capabilities, integrations, ecosystem partnerships, measurement, and cross-functional execution to scale the retail media network and deliver measurable advertiser outcomes while ensuring privacy, data integrity, and launch readiness.

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

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

Lead diverse technology projects and a team to create solutions for regulatory needs, leveraging distributed microservices and collaboration with product managers.

The Senior Product Manager will lead data classification capabilities in a Data Security platform, overseeing product strategy and collaborating with various teams to drive accuracy and compliance.

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 and lead production agentic AI systems for CPQ, including multi-agent orchestration, custom async runtimes, streaming, MCP and A2A interoperability, RAG, and context engineering. Make architecture decisions across state management, tool boundaries, latency, cost, and multi-tenant deployment. Contribute across Python, Java/Spring Boot, and React/TypeScript systems, evaluate classical ML alternatives, set technical direction, and work directly with customers and product teams.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.