Machine Learning Jobs in San Jose, CA

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.

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.

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.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Leads the design, implementation, optimization, and support of SAP IBP and SAP APO supply chain planning solutions. Builds S&OP, demand planning, response and supply, network optimization, and constrained supply models. Applies AI, machine learning, statistical forecasting, demand sensing, anomaly detection, and LLM capabilities to improve planning. Partners with business and IT teams on requirements, integrations, testing, training, prototypes, and production solutions.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Own Samsung’s OPC modeling products by defining product specifications, descriptions, qualification criteria, and feature goals. Partner with R&D and headquarters product engineers on new development, technical requirements, support, acceptance testing, best practices, user guidance, and manuals. The role requires semiconductor and lithography expertise, programming and debugging skills, and daily onsite work in San Jose.

Build and lead NVIDIA’s enterprise data governance platform, covering sensitive-information detection, governed access, export-control enforcement, audit logging, data connectors, and AI knowledge readiness. Design scalable backend, distributed, and data-processing systems; integrate enterprise content sources; evaluate ML classification models; enforce access-control fidelity; and ensure ingestion quality. Provide technical leadership through architecture decisions, code and design reviews, mentorship, and cross-functional alignment with Security, Legal, Finance, and platform teams.

Research, develop, and productionize motion-planning algorithms for autonomous trucks. The role covers decision-making, trajectory generation, optimal control, uncertainty-aware planning, and integration with perception, prediction, controls, and safety systems. Engineers use C++ optimization frameworks and robotics tooling to build safe, comfortable, reliable behavior for complex driving scenarios. Responsibilities also include system diagnostics, algorithm tuning, technical design reviews, and cross-functional collaboration in safety-critical environments.

Lead the technical direction for Zoom’s AI-powered virtual agent quality and reliability. Diagnose failures across models, prompts, and architecture; optimize prompts and prompt chains; build evaluation pipelines; and architect scalable production LLM systems. Collaborate across product, model, and engineering teams, mentor engineers, influence the roadmap, and drive measurable improvements in agent accuracy, resolution rates, and customer outcomes.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Conduct applied computer vision and deep learning research for automated construction drawing takeoff and estimation. Own a scoped research problem, train and evaluate models for detection, segmentation, or structured extraction, explore self-supervised learning and multimodal signals, develop prototypes using real data, design evaluations, and present findings to the research team.

Build and calibrate an agent evaluation platform for multi-step LLM agent trajectories. Design rubrics, deterministic validators, LLM judges, confidence scoring, and human calibration workflows. Fine-tune judge models, analyze simulation-versus-production divergence, and develop process reward models for agent optimization. Establish versioned scenarios, simulated environments, and reliable measurement standards while maintaining production-grade Python code.

Define next-generation data center GPU products by analyzing workloads, especially large language model training and inference. Identify customer opportunities, develop GPU value propositions and product proposals, and collaborate with engineering, operations, field teams, cloud partners, and sales on go-to-market plans, positioning, collateral, and tools. The role requires technical knowledge of GPU, software, and computing architectures, along with 7+ years of technology experience.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Leads market strategy, product roadmaps, marketing plans, competitive analysis, product launches, customer engagement, and market penetration for eBeam computing applications and electron beam products in the semiconductor capital equipment industry. Evaluates customer needs, technical requirements, budgets, ROI, and P&L considerations while influencing cross-functional product development and managing product marketing and management teams. Requires up to 25% travel.

Intern will develop and deploy generative AI applications using large language models, optimize neural networks for NLP and machine perception, and train or fine-tune models for scalable, reliable deployment. The role involves model building, performance optimization, cloud-native deployment, data pipelines, and collaboration within Cisco’s product organization.

Machine Learning Engineer interns develop generative AI applications using GPT-4, Claude, Llama, and transformer models. Responsibilities include building, training, fine-tuning, and deploying neural networks for NLP and machine perception, while optimizing performance, scalability, and reliability. Interns may also work with cloud-native platforms, Docker, data pipelines, AI security, validation techniques, and data science libraries.

Write and edit accessible journalism about AI for a general audience. Responsibilities include pitching ideas, drafting articles, editing researchers’ and writers’ work, setting editorial standards, and helping launch a consistent media program. The founding editor will shape editorial direction and clarify technical material without oversimplifying it.

Research and develop computer vision, sensor fusion, pose estimation, and machine learning solutions for imaging and embedded applications. Build prototypes and end-to-end pipelines in Python and C++, optimize models for real-time edge deployment, and implement MLOps practices for monitoring, versioning, and CI/CD. Evaluate novel algorithms against current research, develop preprocessing and augmentation workflows, and transition validated concepts toward production.

Develop AI/ML solutions for semiconductor design and verification workflows. Build scalable pipelines that extract and analyze EDA logs, reports, run artifacts, and design metadata. Create models, heuristics, and analytics for STA, DRC/LVS, ECO optimization, debug, and regression acceleration. Integrate solutions into CAD infrastructure, automation systems, schedulers, and design databases while collaborating with EDA vendors.

Develop core decision-making and motion-planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, lane changes, merges, path and velocity generation, world modeling, trajectory cost design, optimization, testing, debugging, and root-cause analysis. Collaborate with Prediction, Perception, and Control teams to enable safe, comfortable vehicle motion.