Machine Learning Jobs in Santa Clara, CA

Lead development of machine learning, computer vision, sensor fusion, and registration algorithms for a robotic lung biopsy platform. Prototype, evaluate, and productionize image-analysis solutions in Python/C++, collaborate cross-functionally, perform failure-mode assessment, and maintain state-of-the-art technical knowledge.

Design, train, and evaluate computer-vision perception models (anatomy, instruments, actions) for surgical video; develop temporal/video models and bench-mark SOTA; define perception I/O and move models from offline experiments to robust, real-time OR performance; establish continuous improvement loops, annotation pipelines, and partner cross-functionally to enable prototype-to-product deployment.

Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.

Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

Optimize and deploy ML models to production embedded runtimes. Improve inference latency and efficiency across accelerators, prune/quantize models for memory‑constrained platforms, profile on target hardware, and collaborate with ML and software teams on architecture and integration.

Build and operate end-to-end ML infrastructure: distributed cloud GPU training, datasets, training frameworks, evaluation, deployment, and integrate pipelines into product workflows while collaborating with modeling teams.

Integrate state-of-the-art machine learning (especially generative) into production-grade sensor simulation for LiDAR, Radar, and Camera. Collaborate with research, rendering, and physics teams to develop, evaluate, and deploy models and tooling that produce production value and customer impact.

Develop, prototype, evaluate, and deploy ML-first behavior prediction and planning models to forecast motion and interactions of road users. Build software and infrastructure for behavior-specific ML, collaborate with perception and planning teams, and productionize models for autonomy systems.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

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

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

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

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.

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

Design and implement efficient algorithms to optimize LLM inference on DNN accelerators. Collaborate with mathematicians, ML researchers, and engineers to apply advanced algorithmic and numerical techniques for high-impact generative AI performance improvements.

Lead software engineer responsible for designing high-level and detailed architectures, performance tuning, and building distributed systems. Work with LLM/ML and big-data technologies (e.g., Langchain, Spark), develop in Python and C++, and design data solutions with strong SQL and database fundamentals. Collaborate across teams and integrate diverse frameworks into practical solutions.

Design and develop Bird’s Eye View fusion models for autonomous driving using multimodal sensor inputs like LiDAR, focusing on performance, robustness, and scalability.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead and build a research-focused ML team to develop post-training/RL, dataset curation, LLM pretraining, and sparsity techniques; adapt algorithms to Cerebras hardware, run systematic model evaluation, and collaborate with internal teams and external partners to drive production deployments and hardware/software co-design.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

Design, build, and operate ML infrastructure powering data, compute, artifacts, and orchestration across cloud and on-prem. Own backend services, storage, observability, security, and developer tools; collaborate with cloud/compute providers and lead reliability and scaling efforts.

This role involves full-cycle recruiting for technical talent in fast-paced environments, building partnerships, sourcing from unconventional channels, and improving recruiting systems.