Machine Learning Jobs in Santa Clara, CA

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

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.

The intern will support the data science team by gathering data, performing exploratory analysis, building machine learning models, and developing visualizations to aid business insights.

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.

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

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.

Build, train, and fine-tune offline scene-understanding models adapted from on-vehicle and foundation models. Improve accuracy and generalisation across vehicles, geographies, and conditions. Exploit offline compute and temporal context, benchmark models with statistically defensible metrics for validation and safety, and lead cross-functional work while mentoring teammates.

Architect and optimize timing and power implementation flows for ASIC/FPGA designs. Lead STA and UPF-based power flows, library generation/characterization, automation infrastructure, and apply ML/AI to improve flow efficiency, debug productivity, and signoff QoR while collaborating across PD, STA, library, and CAD teams.

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

Design and implement agent harnesses, evaluation benchmarks, and post-training pipelines. Develop RL environments and reward models, run and optimize multi-GPU training, profile and debug training jobs, and iterate on model fine-tuning to improve capabilities for agentic, edge-aware applications.

Design and build scalable, permission-aware retrieval and reasoning systems for Zoom's AI Companion. Implement vector and hybrid search, RAG pipelines, ranking and evaluation frameworks, indexing, and NLP/entity-extraction pipelines. Collaborate with product, infrastructure, and research teams to deploy production-grade, multi-tenant search and LLM-based applications.

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.

Design, build, deploy, and maintain production-grade AI/ML applications (including LLMs and agentic systems). Implement APIs, services, data pipelines, testing, monitoring, and CI/CD. Collaborate with data scientists, platform, security, and product teams to deliver secure, scalable, observable AI solutions that create measurable business value.

Research and build large-scale neural reconstruction and generative models to complete and enhance 3D digital twins. Develop neural rendering and novel view synthesis methods, design scalable training/inference pipelines for multi-view and 3D data, translate prototypes to production, define evaluation benchmarks, collaborate across teams, and contribute patents and publications.

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.

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

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Build and deploy production-grade AI/ML inference systems for a cloud security platform. Optimize LLM serving (vLLM, SGLang, KV cache), implement model evaluation/reporting, and collaborate with ML scientists and engineers to execute the AI roadmap.

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.

Lead design, development, and deployment of deep learning models for pathology image analysis. Drive experiments, optimize models for production, support data and ML infrastructure, implement semi/self-supervised methods, collaborate with clinical and product teams, and ensure validated, scalable medical device software.

Lead architecture for a hierarchical, multimodal embodied-AI system for surgical robotics. Define interfaces between perception, reasoning, and control; ensure real-time, safety-critical deployment; enable offline-to-online data loops; collaborate across research, engineering, data, and product; mentor senior staff.