Machine Learning Jobs in Sunnyvale, 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 Product Marketing Manager will position CoreWeave's SUNK offering, develop messaging, and collaborate with engineering and sales teams to enhance product adoption and market understanding for AI training workloads.

Lead end-to-end machine learning solutions for autonomous driving, mentor engineers, design advanced models, and ensure safety and reliability in diverse scenarios.

The Staff ML Engineer will design and implement backend components for the ML Inference Platform, ensuring efficient model serving and collaborating with ML teams to enhance AI infrastructure at GM.

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

As a Staff ML Infra Engineer, you will develop and deploy offboard machine learning solutions for autonomous vehicles, ensuring model integration and performance across teams. You'll build ML infrastructure, implement CI/CD pipelines, support data curation, and mentor engineers.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

The role involves designing and implementing advanced analytics solutions for healthcare payers, analyzing complex problems, mentoring juniors, and building client relationships.

Design and implement agentic AI features using LLMs and generative models for conversational and web interfaces. Lead end-to-end delivery, collaborate with PM/UX/GTM teams, expand product reach globally, and iterate on the Agentic AI framework to deliver scalable enterprise value.

Design and implement agentic AI features that apply LLMs to enterprise automation. Lead end-to-end delivery, collaborate with PM/UX/GAI experts, expand product reach, and iterate on conversational and web UI experiences to deliver scalable business value.

Scale and optimize a generative conversational AI platform: build product infrastructure, design scalable APIs for chat clients, improve dialog engine performance, implement logging/tracing/metrics, and collaborate with ML and product teams to support enterprise deployments.

Lead design and implementation of agentic AI and conversational product features, collaborate with GAI framework experts and cross-functional teams, tech-lead engineering delivery, expand product adoption and global reach, analyze feedback to advance the Agentic AI framework, and grow into an LLM-focused product engineering leader.

Design and implement agentic AI features using LLMs and generative models for conversational and web UIs. Lead end-to-end delivery, collaborate with PM/UX/GAI experts, expand product reach across customers and domains, and iterate on the Agentic AI framework to drive enterprise automation and business value.

Lead finance transformation engagements using Oracle Cloud ERP and EPM. Design and implement Oracle Financials and Hyperion solutions, integrate RPA/ML/analytics, ensure compliance, manage stakeholder relationships, coach teams, and drive strategic outcomes on large, cross-border projects.

Build, optimize, and scale end-to-end ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, model evaluation/monitoring, latency optimizations, automation abstractions, and collaborate cross-functionally to productionize state-of-the-art models.

Design, train, evaluate, and productionize LLM-based NLU and agentic AI systems. Improve model quality, latency, reliability, and safety; fine-tune models, implement agent architectures, and collaborate across teams to deploy scalable, privacy-preserving conversational AI.

Lead development and scaling of a generative conversational AI platform: build extensible infrastructure and APIs for chat clients, optimize dialog engine for performance and low memory, implement logging/tracing/metrics and debugging tools, and collaborate with ML and product teams to deliver domain-specific, multilingual enterprise experiences.

Design, build, and optimize scalable ML infrastructure for training, evaluating, and serving large language models. Build abstractions, automation, microservices, and ETL pipelines; collaborate cross-functionally to productionize, monitor, and optimize LLMs and related ML systems.

Lead design and scaling of a generative conversational engine and platform: build scalable APIs, optimize dialog performance and memory/latency, implement logging/tracing/metrics, enable integrations with chat clients, and collaborate with ML and product teams to deliver enterprise-grade agentic AI features.

Build and productionize ML algorithms and high-performance pipelines for large-scale structured, tabular, relational, and graph data. Translate research into prototypes and production-ready systems, design benchmarks and evaluation harnesses, optimize training/inference (cost, latency, throughput), and collaborate across research and engineering to iterate from prototype to production.

Design and own the runtime infrastructure for agentic AI: build an agent orchestration engine, distributed session management, event-driven pipelines, structured-concurrency execution, observability with distributed tracing, and caching/state layers to support long-running, low-latency agent sessions at scale.

Lead a team of engineers to enhance AI-powered search relevance through machine learning. Oversee model development, project management, and team growth while collaborating across departments.

As a Senior ML Infrastructure Engineer, you'll design and build scalable platforms for ML inference workflows, collaborating with teams to optimize model serving and enhance system reliability.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.