Machine Learning Jobs in Sunnyvale, CA

As a Research Product Manager, you will oversee complex research programs, turning technical ideas into execution plans and aligning research with production systems to enhance AI capabilities.

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

Own and deliver a major subsystem of ServiceNow’s exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Lead architecture, design and code reviews, production delivery, metrics, AI safety guardrails, and technical direction. Partner with product, security R&D, and SecOps while mentoring engineers and guiding ambiguous AI/ML problems to scalable, reliable production systems.

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, architect, deploy, and optimize advanced machine learning models for real-time onboard autonomous driving. Lead end-to-end ML projects, improve neural architectures and inference performance, ensure robustness and safety in critical systems, mentor engineers, collaborate across engineering teams, and influence technical roadmaps. The role focuses on foundation models, computer vision, sensor-to-action learning, and onboard autonomy.

Build and operate reliable production systems for machine learning models, agentic workflows, and self-learning capabilities. Responsibilities include ML lifecycle automation, continuous delivery, safe deployments, feedback loops, observability, SLOs, distributed workload optimization, incident response, platform automation, and cloud infrastructure standards. The role provides staff-level technical leadership across ML, data, product, and platform engineering teams.

Design and build scalable backend services and risk intelligence platform components using Golang/Python and microservices. Architect data pipelines, databases, and monitoring; implement high-throughput Kafka-based ingestion and risk calculations; ensure production reliability, performance, and remediation automation; mentor engineers and collaborate cross-functionally to deliver secure, observable systems.

Lead product strategy and roadmap for AI-native capabilities in CrowdStrike's Next-Gen SIEM. Collaborate with engineering, data science, research, marketing, sales, and support to design, deliver, and position AI-driven detection, investigation, and prevention features. Conduct competitive analysis, define use cases, and engage customers to inform product direction while supporting go-to-market and cross-functional execution.

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.

Leads model distillation, parameter-efficient fine-tuning, reinforcement-learning alignment, dataset development, quantization-aware training, and evaluation for multimodal AI models deployed on automotive edge hardware. Sets architectural direction for optimization pipelines, selects foundation models, manages continuous improvement loops, and ensures compact models retain reasoning, vision, language, accuracy, and safety performance after compression.

Lead end-to-end machine learning development for autonomous driving, including data curation, foundation-model pre-training, post-training, fine-tuning, alignment, and deployment. Build scalable pipelines using real and synthetic datasets, apply imitation and reinforcement learning, improve model safety and reliability, collaborate across teams, lead technical initiatives, and mentor ML engineers.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Leads and grows engineering and machine learning teams developing enterprise-scale AI Search, retrieval, ranking, recommendation, personalization, and RAG systems. Owns architecture, strategy, roadmap execution, production deployment, reliability, observability, relevance optimization, and performance. Partners with product, AI, platform, UX, and quality teams to deliver AI-powered experiences for millions of users while mentoring technical leaders and driving innovation.

Develop and ship production machine learning systems, converting research into products and building data diversification, data population, and ML pipeline workflows. Apply LLMs, VLMs, and agentic systems to data generation, define evaluation criteria, and conduct offline evaluations before production changes. Collaborate across engineering and science teams while addressing reliability, latency, cost, fallback, and tool-call recovery challenges.

Leads development of scalable cloud-native platform capabilities for catalog data management. Responsibilities include software design, coding, testing, deployment, monitoring, maintenance, infrastructure and tooling initiatives, AI/ML integration, system design reviews, stakeholder collaboration, mentoring engineers, and continuous improvement. Builds microservices, APIs, and automation using Java, Spring Boot, React, Kubernetes, Python, and JavaScript.

Lead engineering and product efforts to design and implement Agentic AI and conversational/web features using LLMs. Collaborate with ML experts, PM, UX, and GTM to scope, deliver, and scale enterprise AI product capabilities across customers and markets while mentoring engineers and shaping product direction.

Lead product strategy and execution for an enterprise-scale AI-powered search platform. Own platform scalability, search quality metrics, ML performance tradeoffs, cross-functional roadmaps, and translate ambiguous user needs into clear specs and success metrics to deliver performant, enterprise-grade AI search features at scale.

Lead development of a generative-AI-enabled enterprise search platform: architect abstractions, build ranking and multi-turn QA systems, integrate LLM and RAG features, create evaluation metrics, and mentor engineers and hiring processes.

Build and improve enterprise conversational search and question-answering systems using IR and generative AI. Implement retrieval, matching, ranking, and summarization across heterogeneous knowledge sources, develop LLM-enabled features, and create evaluation metrics addressing enterprise constraints like security, privacy, and scalability.

Improve enterprise search relevance for a question-answering platform by crafting and optimizing ranking features, building algorithm frameworks, enabling parallel ranking experiments with tooling, analyzing query logs, and developing metrics to evaluate and boost answer quality across heterogeneous knowledge sources.

Design and build scalable backend services and data pipelines for CrowdStrikes Exposure Management platform. Implement microservices in Golang/Python, process high-volume data, perform risk calculations, integrate cloud infrastructure, ensure reliability and monitoring, mentor engineers, and drive architectural improvements and remediation automation.

Develops high-fidelity product demonstrations, functional prototypes, interactive experiences, front-end solutions, and reusable creative tools for Microsoft marketing. Translates creative concepts into technically feasible, product-accurate experiences for web, social, events, film, and keynotes. Partners with designers, producers, product marketing, and engineering while integrating AI tools, browser interactivity, real-time engines, code-driven animation, plug-ins, and APIs.

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.