Machine Learning Jobs in San Jose, CA

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.

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.

Build and manage a machine learning engineering team focused on making engineering drawings and CAD data usable for automation. Lead hiring, onboarding, mentorship, performance, technical direction, design reviews, and production deployment ownership. Guide annotation tooling, active learning, synthetic data augmentation, model health, and organizational growth while remaining technically hands-on and partnering across engineering, technical leadership, and business teams.

Lead strategic positioning and integration of AI-driven and digital platforms for semiconductor process development. Define end-to-end solutions, orchestrate insight integration, partner with product and engineering, identify gaps and high-value AI use cases, and drive adoption and enablement across internal teams and customers.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Lead a Customer Success team, manage key semiconductor accounts, drive customer adoption, and translate technical challenges into solutions for strategic growth.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

The Data Engineer will gather and process data, analyze trends, support business decisions, and collaborate on predictive models while improving algorithms and efficiency.

Seeking a seasoned Photonics Engineer to lead optical integration efforts in semiconductor testing, ensuring signal integrity and system reliability. Responsibilities include design, testing, and overseeing integration in high-volume manufacturing settings, along with mentorship and project management tasks.

Lead full lifecycle of L4 Robotaxi carline programs, build and manage a carline PM team, coordinate software/hardware/vehicle/validation integration, drive ML/data ops and in-car testing, resolve cross-functional issues, and manage customer relationships through productization to mass-production readiness.

Provides legal counsel for complex commercial, software, SaaS, cloud, intellectual property, and technology transactions. Advises stakeholders on AI governance, regulatory developments, vendor agreements, data usage, licensing, and emerging technology risks. Drafts and negotiates contracts, maintains legal playbooks, delivers training, and partners with business, engineering, IT, cybersecurity, compliance, procurement, and executive teams on domestic and international matters.

Lead the Platform Software Engineering team building and scaling core data infrastructure for research, market-making, and risk platforms. Hands-on role combining individual contributor work, system architecture, cross-functional project leadership, and people management while collaborating with Research and Business Operations.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

Train and evaluate machine learning and deep learning models, diagnose training and generalization issues, conduct experiments, and develop reusable research tooling. The intern may work on LLMs, transformers, generative AI, fine-tuning, and evaluation workflows while rapidly prototyping with AI-assisted development tools. The role requires advanced quantitative coursework and graduate study in computer science, AI, machine learning, statistics, applied mathematics, data science, electrical engineering, or a related field.

Architects, builds, and operates enterprise AI platform services across model serving, inference optimization, evaluation, agentic infrastructure, APIs, and air-gapped deployments. Leads model development, fine-tuning, productionization, MLOps, security governance, and responsible AI practices. Establishes rigorous evaluation and release criteria, mentors senior engineers, drives technical roadmaps, and bridges AI research with reliable production engineering across cloud, on-premises, and resource-constrained environments.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Research and write technically rigorous content about AI, inference, compute, chips, systems, and infrastructure. Responsibilities include long-form analysis, product launches, leadership ghostwriting, customer case studies, technical briefs, social content, and data-driven visuals. The role partners closely with engineers and executives to develop original viewpoints, explain complex concepts, and establish Etched’s technical voice across audiences and channels.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design, build, and own web and mobile applications and backend infrastructure. Implement APIs, database schemas, and scalable microservices. Integrate and productize ML/DL models, leverage code-generation tools, and collaborate with design, data science, and aviation experts to deliver production-ready SaaS features.

Design, build, and own full-stack web and mobile applications and backend infrastructure. Architect scalable APIs, databases, and orchestration services, integrate and productize ML/AI models, collaborate with design and data science, and drive the software lifecycle from prototype to production.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

As a Customer Success Manager, you will lead initiatives to enhance customer outcomes, manage accounts, and collaborate with engineers to optimize our SaaS platform.

Build scalable data and ML systems for LLM training, post-training, evaluation, and continuous improvement. Responsibilities include creating data pipelines, human-in-the-loop labeling workflows, synthetic datasets, quality metrics, and automated evaluation systems. The role applies LLMs and machine learning to data generation and validation, processes large-scale structured and unstructured data, translates research into production systems, and provides technical leadership and mentorship.