Machine Learning Jobs in Sunnyvale, CA

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

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

Join Tapestry's Machine Learning team to build and deploy ML models for the electric grid, collaborating with experts across AI and engineering.

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

As a Machine Learning Engineer, you will apply ML techniques for perception tasks, develop model training methods, and evaluate performance in autonomous driving applications.

The role involves architecting GenAI systems, designing budgeting algorithms, and building infrastructure for experimentation in advertising technology.

Design and operate backend services for large-scale ML inference systems, ensuring performance and reliability at billions of requests daily.

The Senior Machine Learning Infrastructure Engineer will design and maintain infrastructure for ML model serving, ensure systems run reliably, and collaborate with teams to optimize performance and cost-efficiency.

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 deploy state-of-the-art ML models for predicting interactive agent behavior and building controllable agents for closed-loop simulation. Conduct research in generative sequence modeling, transformers, diffusion models, and sequential decision-making, and collaborate with planning and autonomy teams to produce deployable solutions for real vehicles.

Lead an ML Platform engineering team building large-scale GPU cluster architecture, training and inference orchestration, and performance optimization. Own design, scaling, scheduling, networking, and resource management while partnering with research and stack teams to accelerate production deployment and drive hiring and mentorship.

The Senior Software Engineer is responsible for designing, developing and maintaining AI-powered services, enhancing ML capabilities, and leading cross-functional teams in a collaborative environment to innovate and implement best practices in document intelligence.

The role involves developing and maintaining machine learning models for robotics, creating training and deployment pipelines, and collaborating on product needs.

As a Senior ML Engineer at Waymo, you will work on distilling large LLM/VLM models into smaller, real-time models, focusing on enhancing performance for critical semantics tasks. You will explore distillation techniques to optimize quality transfer from larger models and customize datasets accordingly.

The role involves building and managing data infrastructure and pipelines for machine learning, automating model retuning, and ensuring efficient data flow from collection to model training.

The Senior Machine Learning R&D Engineer will innovate in spatial computing, develop ML algorithms, process 3D data, and collaborate with teams to enhance Matterport's platform.

Responsible for designing and developing scalable data pipelines, storage systems, and monitoring tools for ML data infrastructure, ensuring system integrity and performance evaluation.

Design and develop data pipelines for autonomous driving systems, create storage for data metrics, build dashboards, and maintain data integrity, focusing on ML components.

As a Senior Software Engineer at NVIDIA, you will design and implement inference software optimizations for AI applications using TensorRT, collaborating with deep learning experts to influence hardware and software design.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Develop and implement image processing and machine learning algorithms, collaborate with engineers, and demonstrate algorithm performance for semiconductor process development.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.