Machine Learning Jobs in Santa Clara, CA

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

As a Senior Machine Learning Engineer, you'll design and implement large-scale E2E driving models, enhance data workflows, and collaborate with cross-functional teams to advance autonomous driving technologies.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

Lead development of large-scale ranking, personalization, and recommendation systems for Roku's entertainment assistant. Design and deploy ML models (LLMs, transformers, RL, bandits), build feature platforms and data pipelines, run online A/B experiments, and mentor engineers while balancing engagement, retention, relevance, and monetization objectives.

Design, build, and maintain enterprise AI-SDLC platform capabilities and LLM-powered solutions, including prompt orchestration, multi-agent frameworks, API integrations, authentication, CI/CD, automated testing, and governance. Partner with engineering teams to translate requirements, troubleshoot AI pipelines, and ensure secure, compliant, and scalable AI platform operations.

Develop and optimize Python or C++ code to compress, convert, deploy, and run neural network models efficiently on analog compute-in-memory chips. Profile performance, support software design and runtime environments, collaborate with AI researchers and hardware engineers, and participate in code reviews, testing, and documentation.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Optimize large-scale ML training and inference workloads by profiling bottlenecks, implementing efficiency improvements (parallelism, mixed precision, compilation), building observability and benchmarking tools, and collaborating with research and platform teams to scale models and increase training throughput.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead the design, development, and implementation of advanced machine learning models, collaborating with teams to enhance services through AI/ML solutions.

Design, build, and operate high-performance inference platforms for large ML models. Focus on request routing, batching, caching, autoscaling, GPU utilization, observability, and performance engineering to deliver low-latency, high-throughput production serving across diverse model workloads.

Design, build, and optimize scalable deep learning, reinforcement learning, and Bayesian models. Support research projects, develop end-to-end ML systems and software libraries, exploit parallel architectures, and collaborate with product and engineering to influence roadmap and present results.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

The Machine Learning Scientist will lead open-source research, design experiments, develop novel methodologies, and analyze large-scale data to advance AI model evaluation and transparency.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Design and implement distributed cloud GPU training for ML, build end-to-end pipelines, collaborate on data problems, and uphold ML best practices.

Responsible for designing and optimizing VLSI circuits, translating requirements into data science problems, and building machine learning solutions. Collaborate on hardware design and integrate models into existing tools.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

Responsible for architecting high-performance machine learning silicon, collaborating with software and design engineers, and driving innovation in AI hardware solutions.