Machine Learning Jobs in San Jose, CA

Develop efficient ML solutions for perception including sensor fusion, obstacle detection, scene understanding, and robust onboard systems.

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.

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.

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 design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

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.

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.

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.

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.

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.

As a ML Engineer focused on NLP at Avoma, you will improve the Insights Engine, develop new models with LLMs, maintain code quality, and stay updated on NLP advancements.

The Senior Machine Learning Engineer will design and build distributed systems for personalized content recommendations, ensuring deployment and optimization of ML platforms.