Machine Learning Jobs in Santa Clara, CA

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

The Senior Director of Engineering leads engineering strategy, mentors teams, oversees project delivery, collaborates with stakeholders, and champions best practices for operational excellence.

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.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Lead the technical strategy, architecture, and delivery of large-scale machine learning systems for autonomous driving. Develop end-to-end AI methods, define dataset and auto-labeling requirements, mentor senior engineers, influence multiple teams, and integrate autonomy technology across Uber. The role requires expertise in Python, Linux, PyTorch, autonomous systems, and scalable ML infrastructure, with preferred experience in C++, CUDA, ROS, foundation models, and high-performance optimization.

Evaluates emerging machine learning technologies and designs scalable, secure enterprise architectures covering data ingestion, feature engineering, training, deployment, monitoring, governance, and responsible AI. Partners with engineering, data science, platform, risk, and security teams to establish MLOps standards, reusable solution patterns, and production deployment strategies. Provides technical leadership, evaluates vendors, guides implementation, and translates complex ML architecture recommendations for technical and non-technical stakeholders.

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.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Investigate autonomous vehicle issues across hardware, software, robotics, and AI models; analyze fleet and on-road data, identify root causes, reproduce problems through simulation and testing, and develop automated investigation workflows. Collaborate with engineering, operations, safety, and model development teams, respond to incidents, communicate findings, and improve AV performance and safety.

Leads complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product. Owns project delivery, improves efficiency, manages risks and dependencies, aligns stakeholders, supports business goals, and drives continuous process improvement. The role requires strong technical understanding, systems thinking, communication, project management, and influence without authority across engineering teams.

Lead the technical direction, architecture, and delivery of large-scale machine learning systems for autonomous vehicle data. Develop autonomy algorithms, foundation models, deep scene understanding, causal models, high-quality datasets, and auto-labeling systems. Mentor senior engineers, define ML platform requirements, lead complex projects from conception through production, and coordinate integration across Uber engineering 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.

Research and develop end-to-end sensing algorithms for robotics and Physical AI, spanning signal processing, detection, estimation, camera and image processing, computer vision, machine learning, and multisensor fusion. Prototype in Python and C/C++, validate against simulated and real-world sensor data, define performance KPIs, optimize algorithms for embedded platforms, and support hardware, firmware, silicon, customer integration, and product bring-up.

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.

Build scalable evaluation, introspection, visualization, and metrics tools for autonomous vehicle machine learning models. Develop workflows processing billions of examples and connect offline evaluation with data, training, simulation, and launch decisions. Collaborate with modeling, data, infrastructure, and validation teams to improve model performance, safety, reliability, and development speed. Responsibilities include software design, implementation, maintenance, code reviews, and production tooling across backend, frontend, and data infrastructure.

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.

Designs, implements, verifies, and supports motion-control algorithms, robotic manipulation behaviors, and safety-monitoring systems for surgical robots and instruments. The role applies control theory to mechatronic and tele-robotic systems, analyzes clinical and dynamic safety risks, integrates hardware, firmware, software, and mechanisms, supports products through manufacturing, and communicates system performance across the organization.

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.