Machine Learning Jobs in Santa Clara, CA

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.

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, build, and operate AIOps and observability platforms (metrics, logs, traces, alerts, dashboards). Define roadmap and standards, collaborate with teams, mentor engineers, and implement ML/LLM-based anomaly detection, forecasting, root-cause analysis, and agentic debugging across large-scale cloud, on-prem, and bare-metal environments.

Lead R&D for underwater acoustics: define scientific vision, develop detection/classification/localization and acoustic mapping capabilities, guide sensing design and deployments, run field experiments and analyses, translate research into operational products, and represent the company to partners and customers.

Define and position datacenter GPU products for AI, ML, and HPC workloads (especially LLMs). Identify customer needs, translate into product value propositions, lead cross-functional go-to-market planning, and create sales collateral while coordinating with engineering, operations, and field teams.

Develop, test, and document embedded software and firmware for an SoC-based sensor/BLE board. Implement multi-channel data acquisition, optimize signal-processing and proprietary algorithms, create BLE/server communications protocols, build device drivers, perform testing, and produce documentation.

The Technical Program Manager will lead software programs, collaborate with multiple teams, manage dependencies, and drive product delivery to improve engineering velocity and transparency.

As a Senior Machine Learning Scientist, you will develop methodologies for advertising-related challenges, utilizing deep learning and statistical techniques for optimization and experimentation.

Architect and lead the development of a state-of-the-art inference platform focusing on low latency and scalability for advertising. Oversee performance optimization and collaborate with cross-functional teams.

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.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

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

The Staff Machine Learning R&D Engineer will innovate machine learning algorithms for spatial computing, collaborate with teams, and translate research into production solutions.

Lead research and production deployment of ML and computer vision systems for 3D capture and reconstruction. Improve camera pose estimation, mesh/surface reconstruction, texturing, semantic understanding, and real-time capture guidance. Apply deep learning, multi-view geometry, and neural rendering techniques; collaborate across hardware, software, and product teams; and manage the full ML lifecycle from data collection to production monitoring. Write high-quality Python and C++ code.

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

Develop and apply deep learning and data analysis techniques to create scalable vehicle simulation models, collaborating with teams to advance autonomous technology.

Design, build, and scale distributed machine learning platforms supporting personalized content recommendations for millions of users. Responsibilities include developing feature stores, real-time inference services, vector databases, deployment and monitoring infrastructure, online A/B testing, platform optimization, API development, and mentoring engineers. The role requires strong software engineering and computer science fundamentals, proficiency in a high-level language, big data experience, and familiarity with modern ML frameworks and AWS tools.

Lead machine learning strategy and architecture for Roku’s Content Platform. Build and deploy scalable ML products using generative AI and vision models to improve content quality, discovery, and engagement. Guide MLOps infrastructure, bridge AI research with production delivery, partner with Product, Engineering, and Content Strategy, and balance long-term innovation with incremental releases and measurable business outcomes.

Build, train, fine-tune, and evaluate computer vision and scene-understanding models for offline autonomous-driving measurement. Improve accuracy and generalization across vehicles, markets, and conditions using large models, temporal context, and multi-task learning. Define ground truth, benchmarks, metrics, and statistically defensible evidence for validation and safety cases. Collaborate across modeling, evaluation, data, and simulation teams while mentoring engineers and driving significant modeling workstreams.

Lead data-centric work for embodied AI: mine and curate fleet driving data, build large-scale enrichment and labeling pipelines, train and fine-tune foundation models, and design rigorous offline and closed-loop evaluation to improve safety and generalization across cities, vehicles, and sensor rigs.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Develop high-performance software for silicon test pattern generation, silicon application, failure analysis, and yield learning. Create parallel graph traversal and analysis methods, collaborate across technical teams, and apply LLMs, RAG, graph-based machine learning, and reinforcement learning to electronic design automation solutions. The role also involves large-scale model training, data processing, agentic applications, and advanced multi-agent and vector database systems.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.

Lead development of 3D geometric vision and world foundation models for autonomous driving. Responsibilities include designing and training deep-learning models, building scalable sensor-data, auto-labeling, SLAM, reconstruction, evaluation, and ground-truth pipelines, and deploying models on production vehicles. The role requires technical direction across geometric vision, multimodal sensing, distributed training, neural rendering, 3D representations, and real-time inference.

Optimize and deploy LLM inference systems across multi-node clusters to maximize throughput and minimize latency. Implement and benchmark inference optimizations, profile GPU and memory bottlenecks, build evaluation and benchmarking frameworks, and collaborate with research scientists to integrate new model architectures and techniques into production.