Top AI & Machine Learning Jobs

Lead and grow the engineering team productizing and operating an inference offering. Drive roadmap execution with Product, improve reliability, observability, release and incident processes, integrate platform capabilities, and deliver developer-friendly tooling and application-layer enhancements to scale production ML workloads.

Develop and run rigorous benchmarks and profiles for LLM inference workloads to identify bottlenecks and drive targeted performance optimizations across frameworks, runtimes, and GPU hardware. Design experiments (quantization, caching, speculative decoding), validate changes against real traces, partner with platform engineers to productionize improvements, and produce clear technical writeups to guide model-serving, runtime, and hardware decisions.

Serve as the named technical partner for financial services customers, driving adoption and expansion of CoreWeave's AI platform. Provide hands-on support for training, fine-tuning, evaluating, and deploying LLMs and other deep learning workloads; solve technical problems, create demos/PoCs, advise on production performance, reliability, governance, and infrastructure efficiency; collaborate with sales, field engineers, product, and engineering to ensure customer success.

Lead go-to-market and technical strategy for AI developer tooling, winning early customers and building repeatable playbooks. Translate customer MLOps requirements into product feedback, partner with engineering and Weights & Biases, and enable sales/solutions teams to close deals around experiment tracking, model governance, and LLM observability.

Act as the named technical partner for prospective customers, design demos and end-to-end PoCs, advise on best practices for AI workloads, collaborate with Sales and Field Engineering, and influence product roadmap based on customer needs to drive adoption and production deployment of LLM-powered applications.

Lead and grow strategic partnerships across the Physical AI stack (robotics, simulation, autonomous systems). Drive joint product integrations, co-innovation, go-to-market motions, co-sell opportunities, and partner-aligned roadmaps with NVIDIA and key platform/OEM partners. Influence Product and Engineering requirements, execute quarterly business plans, and represent CoreWeave at industry events to accelerate revenue and market adoption.

Build and scale a distributed platform supporting engineering simulations, agentic applications, and physical AI workflows. Responsibilities include designing distributed architecture, developing production Python services, optimizing distributed compute, implementing workflow orchestration, and contributing to CI/CD, testing, deployment, observability, reliability, and incident response. The role also involves mentoring engineers, making technical decisions, collaborating cross-functionally, interviewing candidates, and supporting end-to-end platform operations.

Lead the architectural evolution of an AI platform from monolithic to distributed systems. Design agent-ready services, write production Python, build distributed compute and workflow orchestration, and own CI/CD, testing, deployment, observability, reliability, and scaling. Mentor engineers, establish technical standards, guide cross-team decisions, collaborate with stakeholders, and support hiring. The role requires deep distributed systems, Kubernetes, AWS, workflow orchestration, data processing, security, and production reliability expertise.

The Account Solutions Architect will manage technical relationships with financial services customers, enhancing platform adoption and ensuring customer success with AI workloads deployment and infrastructure solutions.

Serve as the named technical partner for federal customers, designing and deploying AI/LLM workloads under strict compliance (FedRAMP, DoD IL). Lead end-to-end proofs of concept, influence product/security/engineering, build reference architectures and enablement, and support technical strategy for classified and regulated AI environments.