Machine Learning Jobs in San Jose, CA

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Own and deliver a major subsystem of ServiceNow’s exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Lead architecture, design and code reviews, production delivery, metrics, AI safety guardrails, and technical direction. Partner with product, security R&D, and SecOps while mentoring engineers and guiding ambiguous AI/ML problems to scalable, reliable production systems.

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.

Build and operate production machine-learning components for a novel exploitability engine that ranks security work by realistic attack paths. Responsibilities include evidence ingestion, entity resolution, graph construction, probabilistic scoring, testing, evaluation, observability, and data/model reliability. The role involves prototyping AI capabilities, collaborating with product and security teams, contributing to architecture and code reviews, and applying AI safety, security, and guardrails.

Leads the design, development, and productionization of machine learning systems at scale. Builds and validates models, data pipelines, cloud-based ML architectures, and application code; automates testing and deployment; monitors and retrains production models; and ensures high availability, performance, security, governance, and responsible AI practices. Collaborates with Product, Data Science, and Agile engineering teams to deliver optimized ML applications.

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.

Leads and grows engineering and machine learning teams developing enterprise-scale AI Search, retrieval, ranking, recommendation, personalization, and RAG systems. Owns architecture, strategy, roadmap execution, production deployment, reliability, observability, relevance optimization, and performance. Partners with product, AI, platform, UX, and quality teams to deliver AI-powered experiences for millions of users while mentoring technical leaders and driving innovation.

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.

Develop core decision-making and motion planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, path and velocity generation, trajectory optimization, world modeling, trajectory cost design, testing, debugging, and root-cause analysis. The engineer will collaborate with Prediction, Perception, and Control teams to deliver safe, comfortable, and intelligent vehicle motion in complex environments.

Develop predictive and control algorithms for semiconductor manufacturing using physics-based modeling and machine learning. Build scalable sensor-data pipelines, ML surrogates, reduced-order models, and production software integrated with manufacturing tools, control systems, and dashboards. Analyze sensor and metrology data, optimize model performance, and collaborate across physics, engineering, software, and data science teams to deploy intelligent automation solutions.

Leads the functional and technical design of AI-powered Oracle supply chain planning solutions. Translates demand, supply, inventory, MRP, and fulfillment workflows into production-ready agentic applications using OCI, Oracle AI Database, AI Agent Studio, and machine learning platforms. Responsibilities include data pipelines, master data optimization, OTBI and BI Publisher reporting, SQL/PL-SQL and Python development, LLM and RAG solutions, model deployment, integrations, architecture documentation, and delivery through CRP, SIT, and UAT.

Leads requirements, data modeling, transaction frameworks, ETL pipelines, dashboards, and analytical reporting for payment and business systems. Partners with technology and operations teams to maintain data quality, system reliability, and production performance. Diagnoses issues, coordinates resolutions, performs statistical analysis, and contributes to future-state architecture and process improvements.

Supports business intelligence, big data, and AI/ML initiatives by analyzing requirements, developing and configuring solutions, testing applications, supporting production platforms, troubleshooting incidents, and participating in projects and continuous improvements. The role also assists with governance, change management, audits, and SOX controls while providing informal guidance to team members.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Own the full product lifecycle for AI-native insurance agencies and loss prevention services, spanning customer experiences, internal tools, automation, and integrations. Rapidly prototype MVPs with AI-assisted tools, define success metrics, analyze quantitative and qualitative feedback, align cross-functional stakeholders, and ensure regulatory and data security compliance. The role reports to the CEO and requires autonomous product ownership in a fast-moving startup environment.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.

Leads NVIDIA’s enterprise and AI Technical Program Management organization, including team scaling, portfolio strategy, cross-functional program delivery, governance, executive reporting, change management, operational excellence, and vendor partnerships. The role drives AI-first program operations, automation, cloud and data platform initiatives, risk management, adoption, and enterprise technology transformation while partnering with senior engineering, IT, product, business, and executive stakeholders.

Own Hippo’s Quoting and Distribution platform strategy, roadmap, integrations, vendor data ecosystem, and end-to-end product lifecycle. Lead cross-functional delivery, translate business needs into technical requirements, manage partner and API integrations, and improve quote-to-bind conversion and distribution-driven GWP growth. Drive AI-native capabilities across quoting workflows, data enrichment, and partner experiences while aligning executive, engineering, insurance, and distribution stakeholders.

Own Hippo’s quoting and distribution platform strategy, roadmap, integrations, vendor data ecosystem, and AI-enabled capabilities. Lead cross-functional delivery from discovery through launch, improve quote-to-bind conversion and distribution growth, and bridge business, partner, insurance, and engineering teams. The role requires strong product leadership, API and platform experience, stakeholder management, data-driven decision-making, and insurance or financial services expertise.

Build and lead scalable data platforms, ETL frameworks, streaming pipelines, data lakes, warehouses, and transformation tools. Develop Python- and Spark-based systems for extracting, validating, transforming, and analyzing semiconductor testing data. Support self-service machine learning and analytics platforms, real-time processing, custom visualizations, and complex data ecosystems. Collaborate with IT and business teams to define data strategies, gather requirements, plan projects, and deliver production-ready solutions.

Leads architecture and development of machine learning and large language model systems powering AI features for a network digital twin platform. Oversees agentic AI, natural-language workflows, API integrations, roadmap planning, cross-functional engineering execution, production incident response, hiring, mentoring, and performance management.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Analyzes business and data requirements, converts them into machine learning and generative AI problems, integrates data from multiple sources, builds data models and dashboards, performs statistical analysis, develops predictive and AI models, and benchmarks model improvements. The role requires Python expertise, knowledge of data science and visualization tools, generative AI technologies, and relational and non-relational databases.