AI Solutions Engineer, CoreWeave

• 2+ years of experience in Solutions Engineering, ML Engineering, AI Engineering, or a similar customer-facing technical role
• Strong proficiency in Python (bonus: TypeScript/JavaScript) and experience building production-grade ML or GenAI applications
• Hands-on experience training, fine-tuning, evaluating, debugging, and deploying deep learning or LLM-based systems
• Experience with modern GenAI architectures including retrieval-augmented generation (RAG), tool-using agents, evaluation frameworks, and guardrails
• Comfortable designing cloud-native architectures across AWS, GCP, or Azure, with working knowledge of containers, networking fundamentals, and security best practices
• Able to whiteboard distributed training or inference architectures and then implement working prototypes that demonstrate real customer value
• Excellent communication and storytelling skills, with the ability to translate complex technical concepts into business impact
• Thrives in fast-paced environments and can manage multiple customer engagements simultaneously
• Hands-on experience with Hugging Face Transformers, PEFT/LoRA/QLoRA fine-tuning, TRL, Unsloth, or similar fine-tuning stacks. Big plus if experience using RL to improve agentic systems

Desirable

• As an AI Solutions Engineer you will sit at the intersection of cutting-edge AI infrastructure and the world’s most ambitious AI teams
• You will help customers design, deploy, and scale modern ML and GenAI systems using Weights & Biases and CoreWeave’s AI cloud
• This role combines hands-on technical depth with strategic customer engagement — ideal for AI practitioners who enjoy building real systems, solving complex problems, and shaping how AI is developed and operationalized at scale
• Own the technical wins by building proof of value (PoVs). You’ll be partnering with Account Executives to drive discovery, solution design, and successful technical evaluations
• Lead deep technical conversations with ML engineers, researchers, and platform teams to understand their AI/GenAI workloads and architect scalable solutions across the CoreWeave and Weights & Biases ecosystem
• Design and deliver compelling live demos, proof-of-value engagements, and rapid prototypes showcasing experiment tracking, distributed training, agentic workflows, fine-tuning, evaluation, and inference at scale
• Architect modern ML and GenAI systems including distributed training, RAG pipelines, multi-agent systems, LLM fine-tuning workflows, and inference solutions
• Translate business objectives into measurable technical outcomes such as model quality improvements, cost reductions, performance gains, and faster iteration cycles
• Serve as a trusted technical advisor, influencing architectural decisions and best practices across MLOps, LLMOps, and platform engineering
• Partner closely with Product and Engineering to relay customer feedback, shape roadmap priorities, and improve the end-to-end AI developer experience