Machine Learning Jobs in San Jose, CA

Build and deploy video recommendation models and APIs, research LLM and computer vision applications, and develop semi-automatic annotation techniques for NLU models. Retrain and evaluate machine learning systems, improve Roku Voice data annotation pipelines, collaborate with engineering and product teams to launch research into products, and document technical work through design documents, demos, and presentations.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Lead research, design, development, and implementation of image-processing algorithm modules. Analyze problems, gather data, review literature, prototype solutions, and integrate algorithms into products. Apply computer vision, machine learning, and deep learning for image analysis, pattern recognition, metrology, and object detection. Optimize algorithms for computational efficiency and hardware requirements while managing projects, teams, priorities, and resources.

Own and extend NVIDIA’s production linear programming supply chain optimization model. Formulate constraints and objectives, analyze dual variables and sensitivities, maintain data integrity, and translate semiconductor supply-chain realities into mathematical models. Partner with procurement, operations, engineering, and leadership to evaluate constraints, scenarios, sourcing, capacity, and planning decisions. Evolve the optimization architecture and explore AI, machine learning, GPU acceleration, and cuOpt enhancements.

Lead forward-deployed engineering for Cisco’s AI platforms by embedding with customers, identifying workflow challenges, and rapidly building production-ready prototypes. Apply expertise in networking, security, distributed multi-tenant systems, authorization, observability, and scalable service architecture. Influence product roadmaps through usage data, transition prototypes into secure SaaS and on-premises services, provide technical consultation, and mentor teams while promoting customer-first engineering practices.

Design, engineer, and optimize protein-based biosensors that convert neuronal activity into imaging signals. Build and characterize protein variants using computational design, directed evolution, combinatorial libraries, mammalian expression systems, and high-throughput assays. Collaborate with machine learning, biochemical, structural biology, and computational teams to model sequence-function relationships, develop screening methods, understand biomolecular mechanisms, and advance constructs toward in vivo applications.

Leads multifunctional initiatives defining safety architectures and requirements for autonomous vehicles. Develops safety concepts and analysis supporting continuous software delivery, improves safety metrics and analysis tools, evaluates safety data, and communicates readiness results. Collaborates with perception, AI, motion planning, and architecture experts to ensure DRIVE products meet rigorous safety standards.

Manage NVIDIA’s global Lenovo OEM account and drive adoption and integration of NVIDIA accelerated computing platforms. Develop technical expertise across AI hardware, networking, software, and reference architectures; identify solution opportunities; package NVIDIA technologies into Lenovo offerings; and develop co-marketing and co-selling strategies. Coordinate product, engineering, supply planning, pricing, quality, and go-to-market teams while building relationships with senior stakeholders and supporting cloud and enterprise sales channels.

Translate business objectives into actionable data analysis, analyze large datasets, apply statistical and machine learning methods, and clearly communicate findings to technical and non-technical audiences. The role requires Hadoop ecosystem expertise, Python scripting, strong statistical knowledge, sampling and analytical skills, and business domain experience in retail, enterprise mobility, or healthcare.

Build and operate production machine-learning components for a novel exploitability engine that ranks security work by realistic attack paths. Responsibilities include evidence ingestion, entity resolution, graph construction, probabilistic scoring, testing, evaluation, observability, and data/model reliability. The role involves prototyping AI capabilities, collaborating with product and security teams, contributing to architecture and code reviews, and applying AI safety, security, and guardrails.

Own new business and account growth across hyperscalers, neoclouds, enterprises, and frontier AI labs in the Americas. Manage complex technical sales cycles, layered OEM/ODM and channel routes, capacity commitments, design-ins, pipeline forecasting, contracts, and executive relationships. Translate semiconductor performance, power, and TCO advantages into commercial outcomes while partnering with product, engineering, marketing, legal, and finance. Build a qualified pipeline through an existing customer network and represent the company at industry events.

Design, develop, validate, and optimize algorithms for mass spectrometry data analysis. Responsibilities include feature finding, signal processing, peak detection, pattern recognition, machine learning, numerical modeling, production software development, algorithm validation, performance benchmarking, and collaboration with software engineers, scientists, application experts, and customers.

Develop and apply physics-based and machine-learning models for plasma etching, deposition, and surface modification processes. Use quantum chemistry, molecular dynamics, kinetic Monte Carlo, and HPC simulation datasets to predict plasma-surface interactions. Collaborate with experimental teams, integrate simulation and measurement results, maintain modeling software, validate frameworks, and communicate findings to multidisciplinary teams and customers.

Design, train, evaluate, and deploy computer vision and machine learning models for ground and onboard flight systems. Build the image and ML data engineering foundation, optimize models for low-latency cloud and edge execution, develop benchmark implementations, and support cloud serving integration. Own verification, validation, unit testing, integration testing, and performance regression testing while collaborating with mission and payload teams.

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.

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

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.