Principal Solution Specialist, Infrastructure

Weights & Biases Weights & Biases · Data AI · Bellevue, WA +5 · Global Field Organization

This role focuses on bringing CoreWeave's AI developer services, such as MLOps platforms and LLM observability tools, to market. It involves defining commercial and technical strategies, driving adoption with early customers, and translating field insights into product roadmap requirements. The role requires deep expertise in the ML development lifecycle, LLM application patterns, and MLOps ecosystem, with a focus on experiment tracking, model lifecycle governance, and observability.

What you'd actually do

  1. Own the commercial and technical strategy for net new customer wins in AI developer tooling, where experiment reproducibility, model lifecycle governance, and LLM application observability are the primary buying triggers.
  2. Drive new business opportunities where missing experiment tracking infrastructure, model versioning gaps, or LLM observability blind spots are barriers to scaling AI development on CoreWeave.
  3. Build deep expertise across the AI developer tooling landscape (MLOps platforms, model registries, evaluation frameworks, and LLM tracing infrastructure), using Weights & Biases Models and Weave as flagship examples of what best-in-class developer services look like on CoreWeave.
  4. Translate customer requirements around experiment management, dataset versioning, model promotion workflows, and LLM trace analysis into specific product feedback that shapes the AI Developer Services roadmap.
  5. Develop deal structures, technical playbooks, and capability narratives that help sales and SA teams accelerate opportunities where developer experience and MLOps maturity are key evaluation criteria.

Skills

Required

  • ML engineering
  • MLOps
  • AI platform development
  • customer outcomes
  • revenue generation
  • experiment tracking
  • model registry
  • LLM observability platforms
  • Weights & Biases (Models and/or Weave)
  • customer-facing capacity
  • deal-shaping capacity
  • ML development lifecycle
  • experiment management
  • hyperparameter optimization
  • dataset versioning
  • model evaluation
  • LLM application development patterns
  • prompt engineering workflows
  • RAG pipelines
  • agent evaluation
  • LLM observability requirements
  • enterprise ML platform teams
  • model governance
  • audit requirements
  • scaling AI development
  • benchmarking tooling capabilities
  • commercial positioning of tooling
  • MLOps maturity requirements
  • MLflow
  • DVC
  • Kubeflow
  • Ray

Nice to have

  • driving new business
  • shaping product strategy
  • generative AI product companies
  • enterprise AI teams
  • pharmaceutical R&D
  • financial services modeling groups
  • technical sales
  • solution consulting
  • product management
  • MLOps platform adoption
  • AI developer tooling decisions
  • infrastructure choices
  • platform lock-in
  • Advanced degree in Computer Science, Machine Learning, or Engineering
  • equivalent experience

What the JD emphasized

  • 10+ years of experience in ML engineering, MLOps, or AI platform development
  • 5+ years working with experiment tracking, model registry, or LLM observability platforms
  • Deep working knowledge of the ML development lifecycle
  • Strong familiarity with LLM application development patterns
  • Experience working with enterprise ML platform teams on model governance, audit requirements
  • Ability to benchmark, explain, and commercially position developer tooling capabilities

Other signals

  • developer experience
  • AI developer tooling
  • MLOps platforms
  • model governance
  • LLM application observability