Machine Learning Jobs in Santa Clara, CA

Develop scalable sensor-data pipelines, physics-based models, machine-learning algorithms, predictive controls, and intelligent automation for semiconductor manufacturing. Build production-ready software, ML surrogates, and reduced-order models that integrate with manufacturing tools, control systems, and dashboards. Collaborate across physics, engineering, software, and data science teams to optimize real-time process performance and deploy innovative solutions.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Build scalable data pipelines and production AI systems for networking and security telemetry. Develop ETL/ELT workflows in Python and SQL, create and monitor LLM, RAG, and machine learning applications, and support anomaly detection, troubleshooting, and incident analysis. Collaborate with engineers and researchers on maintainable code, dashboards, design reviews, and live production services.

Develop and deploy machine learning datasets, models, evaluation systems, and MLOps tools focused on content safety, AI security, robustness, fairness, and responsible behavior for multimodal LLMs. Research bias detection and mitigation, define safety metrics, improve model monitoring and automation, and collaborate with engineering, data science, and research teams. The role also involves addressing harmful content, misinformation, hallucinations, adversarial robustness, and GenAI security.

Advance learning-based 3D reconstruction for photorealistic simulation environments using Gaussian Splatting, NeRFs, and related computer vision techniques. Develop dynamic scene reconstruction, novel view synthesis, multi-sensor rendering, scalable feed-forward models, evaluation benchmarks, and production-quality ML systems. Collaborate with research, infrastructure, autonomy, and product teams while solving customer challenges and influencing architecture.

Develop and productionize diffusion and video generation models for realistic, controllable autonomous-driving simulation. Build multi-camera generation, scenario augmentation, neural reconstruction, scalable training and inference, and evaluation benchmarks. Collaborate with research, infrastructure, autonomy, product, and customers to deliver generative simulation capabilities and influence technical architecture.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Lead research, design, and implementation of machine learning algorithms for computer vision, multimodal data, generative AI, and robotics in surgical platforms. Develop and evaluate vision-action models, datasets, validation methods, and scalable pipelines; integrate algorithms into robotic systems; advance research through patents and publications; and collaborate with engineers, clinicians, users, and academic partners to develop AI technologies for robotic-assisted surgery.

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

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Develop and scale distributed machine learning infrastructure for large-scale LLM pre-training. Responsibilities include extending frameworks such as DeepSpeed and FSDP, implementing distributed optimizers, managing multi-node GPU launches, building experiment tracking and monitoring, improving reliability and performance, and maintaining data-loading and checkpointing workflows. The role requires strong Python, systems engineering, distributed training, multi-node debugging, and large-scale pre-training experience.

Build and scale end-to-end reinforcement learning infrastructure for foundation models. Responsibilities include extending distributed training frameworks, integrating rollout and inference systems, implementing reward and trajectory processing, configuring multi-node GPU launches, monitoring experiments, optimizing performance, and developing checkpointing and failure-recovery systems. The role requires strong Python and ML systems engineering experience, hands-on LLM RL pipeline ownership, and expertise with distributed training and policy optimization methods.

Develop optimized GPU algorithms and high-performance machine learning solutions for vector search, databases, and data processing. Responsibilities include designing, implementing, benchmarking, debugging, and optimizing C++, CUDA, and Python libraries; developing clustering and visualization algorithms; collaborating cross-functionally; and contributing to open-source projects such as cuML, cuVS, and RAFT.

Develop core decision-making and motion planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, path and velocity generation, trajectory optimization, world modeling, trajectory cost design, testing, debugging, and root-cause analysis. The engineer will collaborate with Prediction, Perception, and Control teams to deliver safe, comfortable, and intelligent vehicle motion in complex environments.

Develop predictive and control algorithms for semiconductor manufacturing using physics-based modeling and machine learning. Build scalable sensor-data pipelines, ML surrogates, reduced-order models, and production software integrated with manufacturing tools, control systems, and dashboards. Analyze sensor and metrology data, optimize model performance, and collaborate across physics, engineering, software, and data science teams to deploy intelligent automation solutions.

Leads the functional and technical design of AI-powered Oracle supply chain planning solutions. Translates demand, supply, inventory, MRP, and fulfillment workflows into production-ready agentic applications using OCI, Oracle AI Database, AI Agent Studio, and machine learning platforms. Responsibilities include data pipelines, master data optimization, OTBI and BI Publisher reporting, SQL/PL-SQL and Python development, LLM and RAG solutions, model deployment, integrations, architecture documentation, and delivery through CRP, SIT, and UAT.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Own the full product lifecycle for AI-native insurance agencies and loss prevention services, spanning customer experiences, internal tools, automation, and integrations. Rapidly prototype MVPs with AI-assisted tools, define success metrics, analyze quantitative and qualitative feedback, align cross-functional stakeholders, and ensure regulatory and data security compliance. The role reports to the CEO and requires autonomous product ownership in a fast-moving startup environment.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.

Lead the technical roadmap, architecture, and execution of machine-learning behavior prediction and planning systems for autonomous vehicles. Develop production ML algorithms that generate driving trajectories, guide architecture reviews, collaborate with perception, planning, and research teams, and mentor engineers. The role requires hands-on technical leadership across model development, evaluation, deployment, optimization, and scalable software infrastructure.

Own Hippo’s Quoting and Distribution platform strategy, roadmap, integrations, vendor data ecosystem, and end-to-end product lifecycle. Lead cross-functional delivery, translate business needs into technical requirements, manage partner and API integrations, and improve quote-to-bind conversion and distribution-driven GWP growth. Drive AI-native capabilities across quoting workflows, data enrichment, and partner experiences while aligning executive, engineering, insurance, and distribution stakeholders.

Own Hippo’s quoting and distribution platform strategy, roadmap, integrations, vendor data ecosystem, and AI-enabled capabilities. Lead cross-functional delivery from discovery through launch, improve quote-to-bind conversion and distribution growth, and bridge business, partner, insurance, and engineering teams. The role requires strong product leadership, API and platform experience, stakeholder management, data-driven decision-making, and insurance or financial services expertise.

Develops large-scale interactive web-based visual analytics and profiling tools for AI/ML workloads. Builds visualizations for massive telemetry datasets, partners with compiler and hardware teams, and improves workflows for diagnosing machine learning performance bottlenecks. The role requires extensive experience with data visualization, developer tools, profiling systems, computing architectures, and technical user experience design.

Conduct graduate-level research on cyber attack and defense, focusing on threat detection. Study adversary behavior, develop detection logic and research tooling, and convert research problems into working prototypes for a security platform. Collaborate with security researchers and engineers while applying expertise in malware, network traffic, vulnerability exploitation, threat hunting, reverse engineering, offensive security, machine learning, and penetration testing.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.