Machine Learning Jobs in San Jose, CA

Own end-to-end backend IC design automation flow bring-up for new semiconductor process nodes, including PDK, standard-cell library, Foundation IP, EDA tool, script, and infrastructure integration. Develop regression tests, qualify and deploy production flows, support design teams through tape-out, and provide debugging, training, documentation, and methodology improvements. Collaborate across global engineering teams and apply AI/ML to regression analysis, flow optimization, and knowledge capture.

Supports FP&A and enterprise analytics by developing scalable Power BI reporting, financial models, dashboards, and automated processes. Analyzes financial and operational datasets to identify trends, risks, and opportunities; improves forecasting, budgeting, and recurring reporting workflows using Excel, Python, SQL, Power Automate, and Microsoft platforms. Collaborates with finance, data engineering, IT, and business stakeholders, trains users, maintains governance standards, and contributes to the Pigment planning platform. Evaluates AI-enabled tools to improve reporting efficiency, data quality, and decision-making.

Lead global business development for Advantest Cloud Solutions, driving strategy, customer engagement, solution selling, proposals, pricing, negotiations, revenue, profitability, and account growth. Partner with sales, marketing, solution integration, pre-sales, and customer support teams to customize value propositions, address customer needs, manage budgets, and support real-time data infrastructure and analytics solutions for semiconductor customers.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead the architecture, implementation, integration, and optimization of enterprise business systems across finance, supply chain, billing, and customer platforms. Develop system roadmaps, standardize tooling, automate workflows using middleware and AI, ensure SOX and ITGC compliance, troubleshoot complex issues, build reporting dashboards, and mentor team members. The role requires close collaboration with stakeholders and senior leadership to deliver scalable enterprise solutions.

Lead technical program management for NVIDIA GPU software, coordinating chipset and product roadmaps with software capacity. Manage cross-functional dependencies, prioritization, planning gaps, hardware and release-planning partnerships, process improvements, and execution metrics. The role requires delivering software products while balancing short-term critical timelines with long-term improvements to planning, processes, and team capabilities.

Analyze emerging AI workloads and build analytical performance models and architecture simulators for current and future AI inference accelerator hardware. Collaborate with hardware, compiler, inference server, kernel, and product teams to validate assumptions, identify HW/SW optimization opportunities, incorporate architecture research, and document methodologies. The role is a hands-on technical contributor focused on performance analysis across the hardware/software boundary.

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.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Full-time hybrid summer technology internship at Johnson & Johnson from May 24 through August 13, 2027. Interns contribute to technology projects across software engineering, data engineering, AI and machine learning, cybersecurity, cloud and DevOps, UX, digital health, platform engineering, and connected devices. Responsibilities include building solutions, analyzing data and processes, collaborating with stakeholders, supporting project portfolios, and developing technology capabilities. The program may serve as a feeder into the Technology Leadership Development Program.

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.

Leads end-to-end execution of complex software platform programs across cloud infrastructure, platform services, system integration, validation, release delivery, and operational support. Coordinates software, infrastructure, product, and operations teams; manages dependencies, risks, technical issues, schedules, metrics, and senior-leadership reporting. Ensures full-stack readiness and delivery of scalable, reliable software supporting AI workloads.

As a Senior Machine Learning Engineer, you will develop advanced machine learning and deep learning models and platforms for optimizing advertising performance and conduct complex experiments.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

The Software Intern at Cadence will work on research and development using machine learning technologies for on-chip parasitic modeling in a team environment.

Design and develop Multi-Physics System Analysis software, debug errors, implement features, and write tests.

Drive planning and execution of multiple software and ML workstreams, manage cross-functional dependencies, gather requirements, track schedules, support development-to-deployment, and improve processes to deliver customer product releases.

Design, build, and operate production-grade agentic AI systems: multi-agent orchestration, retrieval-grounded reasoning, model integration, observability and governance, and production search/RAG pipelines at enterprise scale.

Lead the design, development, and deployment of AI software components while partnering with engineers and scientists to deliver innovative AI solutions at Capital One.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Design and implement production-grade VLSI/EDA tools and methodologies for multi-die package design. Build flows, applications, and automation to construct, analyze, validate, and optimize package designs to accelerate tape-out and scale engineering teams.

Lead strategic, data-driven projects to optimize operations and supply chain processes. Analyze large datasets, build statistical and ML models, create Power BI dashboards and prototypes, clean and maintain data pipelines, communicate insights to stakeholders, drive implementations, and deliver training using generative AI tools.

Administer and continuously improve the OneStream finance platform: design dimensional and cube architecture, manage business rules, integrations, security, release promotion, and production support. Partner with Finance, Accounting, IT, and external consultants to support consolidation, forecasting, reporting, SOX controls, training, and process improvement across development, test, and production environments.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.