Machine Learning Jobs in Sunnyvale, CA

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.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

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.

Design and lead production agentic AI systems for CPQ workflows, including multi-agent orchestration, async execution, streaming, state management, tool boundaries, MCP and A2A interoperability, RAG, and context engineering. Own the agent execution Harness, make architecture and performance decisions, support classical ML pipelines, and provide full-stack integration across Python, Java/Spring Boot, React, TypeScript, and Lit. Work directly with customers and product teams to deploy solutions and set technical direction.

Source and screen specialized technical and executive candidates across AI/ML research, robotics, computer vision, software, consumer electronics, and related engineering fields. Develop multi-channel sourcing strategies, build talent networks, conduct market research, analyze sourcing metrics, engage passive candidates, and partner with recruiters and hiring managers. The role also supports employer branding, diversity sourcing, candidate qualification, and recruitment process coordination.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Own perception data operations for autonomous driving, translating ML and engineering requirements into labeling deliverables. Manage vendor allocation, budgets, timelines, quality, throughput, and risk across sensor data pipelines. Partner with perception engineering, product, data engineering, and external labeling providers to ensure reliable, high-quality data delivery. Use SQL, and optionally Python, to analyze operational metrics, identify root causes of quality issues, and improve vendor performance and workflow outcomes.

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.

Lead reliability strategy and execution for Waymo’s critical release pipelines. Drive incident response, retrospectives, disaster recovery, capacity planning, observability, and continuous improvement. Harden production software, develop playbooks, improve on-call metrics, investigate novel events, and contribute to robust, debuggable system architecture. Partner across engineering organizations to promote reliability practices for large-scale autonomous vehicle software systems.

Leads and mentors a data science team building production-grade solutions for electric grid visibility and resilience. Oversees data pipelines, curation, annotation quality, metrics, experimentation, model evaluation, and advanced analytics. Partners with engineering, product, power systems experts, executives, and external stakeholders to define strategy, communicate insights, and deliver ML-powered products using rigorous statistical and engineering practices.

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.

Creates technically accurate marketing content and demonstrations, translates complex product concepts into customer-focused messaging, and supports launches, campaigns, webinars, events, developer outreach, partner programs, and sales enablement. Tracks content performance and audience feedback, maintains project and launch assets, presents to technical and business audiences, and collaborates with engineering, product, public relations, creative, and marketing teams.

Develop test software and automation for electrical testing of laser diodes and VCSELs. Build signal waveform preprocessing, analysis, and post-processing solutions for high-speed communications, including PAM4 metrics. Apply image processing and machine learning to testing methodologies, interface with hardware, and collaborate with engineering teams to deploy manufacturing test solutions. Maintain proprietary test-suite software using C/C++, .NET languages, Python, and related technologies.

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 development and production deployment of machine learning, generative AI, and multi-agent solutions. Translate business problems into scalable AI products, build models and data pipelines, architect cloud and lakehouse systems, and implement MLOps, LLMOps, evaluation, governance, security, and monitoring. Partner with cross-functional stakeholders, communicate technical outcomes, troubleshoot production issues, mentor teammates, and shape the strategic roadmap for enterprise AI capabilities across Azure, Databricks, AWS, and GCP.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Develop machine learning and hybrid physics-data methods to improve vehicle and component models. Build scalable pipelines for vehicle telemetry, simulation, test, and fleet data, including ingestion, synchronization, transformation, storage, and evaluation. Create cloud infrastructure for model training, simulation, validation, experiment tracking, and reproducible development supporting vehicle intelligence and autonomous driving.

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 NVIDIA’s AI for Quantum product strategy, roadmap, and go-to-market initiatives. Translate AI and quantum research into scalable platforms, developer tools, APIs, libraries, and workflows. Partner with researchers, engineers, quantum hardware companies, institutions, and developers to identify applications across quantum design, control, calibration, error correction, and algorithms. Evangelize the platform through technical communications, identify new AI, HPC, and quantum opportunities, and present product strategy to executives.

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.

Conduct PhD-level research combining fluid dynamics, multiscale physics, and machine learning for materials discovery and manufacturing. Develop physics-informed, autodifferentiable architectures that replace computationally intensive forward solvers, parameterize unresolved physics, and scale SciML frameworks. Residents may work with laboratory instrumentation and physical experimentation, applying methods such as PINNs and Neural Operators to complex systems including turbulence, multiphase flow, and inverse design.

Leads global digital quality and intelligence strategy across quality and regulatory affairs. Oversees analytics, automation, AI, eQMS support, QMS modernization, governance, validation, roadmap execution, and inspection readiness. Directs global data, analytics, and software teams; manages portfolios, resources, risks, executive reporting, adoption, and change leadership. Ensures responsible AI, data governance, model oversight, and compliant deployment within regulated life sciences environments.

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.

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.