Machine Learning Jobs in Santa Clara, CA

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.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

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 define architectures for complex embedded SoCs, translating system requirements into optimized chip requirements and custom logic. Research and evaluate vendor IP, maintain architecture documentation, participate in design and test-plan reviews, define coverage and performance targets, and support silicon bring-up, system validation, debugging, and implementation. The role collaborates across systems, software, design, and verification teams.

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 Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

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.

Lead technical strategy and architecture for application security across WAF, WAAP, DDoS, API security, bot defense, TLS, identity, and AI/ML detection. Drive modernization to cloud-native, SaaS, and API-driven services, enforce secure-by-design patterns, mentor senior architects, perform threat modeling, and align cross-functional teams on compliance, telemetry, and scalable runtime defenses.

Lead research and development of large-scale threat detection capabilities, analyze emerging attacks and adversary TTPs, build research systems (APT simulation, CTI, security knowledge graph), and collaborate with data scientists to apply agentic AI/ML for security detection workflows.

Lead product strategy and execution for ML training workflows and platforms. Own roadmap, define requirements, measure KPIs for developer productivity, training reliability, experiment management, and cost. Partner with engineering, infrastructure, data, finance, and autonomy teams to prioritize investments, reduce cycle time, and deliver scalable tooling. Mentor product peers and communicate tradeoffs and metrics to senior leaders.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.