Machine Learning Jobs in San Jose, CA

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

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.

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

Design and implement digital ASIC logic across the full ASIC lifecycle: specifications, microarchitecture, RTL (SystemVerilog), simulation, synthesis, static timing analysis, timing closure, linting, CDC, and top-level integration. Leverage GenAI and agentic tools, collaborate cross-functionally, and support interfaces such as PCIe, NVMe, DRAM, NAND, and AXI.

Lead the Software Asset Management engineering team, set technical direction, manage product development and release cycles, drive AI/ML and telemetry initiatives, improve test automation and performance, mentor engineers, coordinate cross-functional and global teams, and ensure scalable, high-quality enterprise solutions with 24x7 support alignment.

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.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

Lead design and deployment of enterprise-scale multi-agent AI systems across the semiconductor lifecycle. Define strategy, architect agentic AI platforms (LLMs, RAG, knowledge graphs), identify automation opportunities, establish AI governance, integrate with EDA and operations systems, mentor technical leaders, and represent the company externally.

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

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

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.

Lead finance transformation engagements using Oracle Cloud ERP and EPM. Design and implement Oracle Financials and Hyperion solutions, integrate RPA/ML/analytics, ensure compliance, manage stakeholder relationships, coach teams, and drive strategic outcomes on large, cross-border projects.

The role involves leading data science initiatives, building machine learning models, conducting experiments, and collaborating with teams to enhance customer experiences and business outcomes.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

The AI Research Engineer will design, implement, and improve AI systems for therapy, enhance evaluation methods, and collaborate with various teams to innovate and ensure safety in AI therapy.

Lead the architecture and deployment of LLM-powered conversational chatbot systems, optimizing performance and ensuring scalability in a crypto environment.

Lead the development and integration of advanced AI-driven software systems for next-generation electric vertical takeoff and landing aircraft, ensuring reliability and safety in collaboration with cross-functional teams.