AI Solutions Engineer, Pre-Sales- W&B

About The Role

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 Core Weave’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 Core Weave 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.

• 2+ years of experience in Solutions Engineering, ML Engineering, AI Engineering, or a similar customer-facing technical role.
• Strong proficiency in Python (bonus: Type Script/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.

• Experience building and evaluating agentic applications using frameworks such as Lang Chain, DSPy, Llama Index, CrewAI, Open Claw, or similar ecosystems. Ability to quickly adapt to new frameworks is a huge plus.
• Familiarity with vector databases, embeddings workflows, and LLM evaluation frameworks (e.g., Ragas, Tru Lens, Deep Eval).
• 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.
• Experience operating distributed workloads using Slurm, Ray, or Kubernetes.
• Experience with inference optimization and serving frameworks such as vLLM, Tensor
  
  RT-LLM, Triton, or TGI.
• Experience in MLOps/LLMOps platform engineering or supporting enterprise AI deployments.

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we…