Account Solutions Architect - Greenfield

Weights & Biases Weights & Biases · Data AI · Seattle, WA · Global Field Organization

This role is a customer-facing technical partner for existing AI customers, focusing on deepening platform adoption, identifying expansion opportunities, and serving as a trusted advisor for scaling AI workloads in production. It involves hands-on experience with training, fine-tuning, evaluating, and deploying deep learning models and LLM-powered applications.

What you'd actually do

  1. Owns the technical relationship with every CoreWeave customer.
  2. Partner with Sales, Support, Product, and Engineering to deliver technical success across the full customer lifecycle.
  3. Work hands-on with some of the most advanced AI teams in the world as they build, train, deploy, and scale their workflows.
  4. Help AI teams solve real-world problems by deepening platform adoption, identifying and driving expansion opportunities, strengthening relationships with key technical stakeholders, and serving as a trusted advisor as customers scale their AI workloads in production.
  5. Represent the voice of the customer internally, surface product feedback from the field, and proactively address technical blockers and business-critical needs to ensure customer success.

Skills

Required

  • 4+ years of relevant experience in a solutions engineer, AI-oriented solutions consultant, or technical field engineering role
  • Proficiency in Python
  • Hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures
  • Experience designing and deploying production LLM-powered applications for customer use cases
  • Familiarity with running AI workloads least one major cloud platform (AWS, GCP, or Azure)
  • Demonstrated ability to break down and solve complex, often novel, technical problems with enterprise customers
  • Excellent written and verbal communication and presentation skills, with the ability to translate technical concepts for both engineering and executive audiences

Nice to have

  • Working knowledge of cloud infrastructure for AI workloads, including GPU compute, high-performance networking, and storage
  • Familiarity one or more deep learning frameworks (PyTorch) and modern LLM stack (VLLM, langchain / LlamaIndex)
  • Experience using Slurm or Kubernetes for ML job orchestration
  • Experience with hyperparameter optimization and experiment tracking tools
  • Background in ML Engineering, AI Engineering, MLOps, or LLMOps
  • Prior experience in a technical pre-sales or solutions architecture role focused on net-new logos or greenfield accounts
  • Familiarity with high-performance GPU infrastructure (e.g., NVIDIA H100/H200/B200, InfiniBand networking, parallel file systems)

What the JD emphasized

  • Hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures
  • Experience designing and deploying production LLM-powered applications for customer use cases
  • customer-focused AI practitioners
  • scale the next generation of AI workloads in production

Other signals

  • customer-facing technical role
  • deploying production LLM-powered applications
  • scaling AI workloads in production
  • deep learning models
  • LLM architectures