Manager, Solutions Architect

Baseten · Data AI · San Francisco, CA · EPD

Manager for a Solutions Architect team focused on enabling customers to deploy and optimize AI/ML models, particularly LLMs, on Baseten's inference platform. The role involves leadership, technical guidance, customer discovery, and ensuring high performance, reliability, and cost efficiency of AI applications in production.

What you'd actually do

  1. Lead, mentor, and grow a team of Solution Architects, providing guidance on technical direction, project execution, and professional development.
  2. Set clear goals and ensure timely, high-quality delivery across multiple customer-facing projects involving LLM deployment and inference optimization.
  3. Collaborate with leadership to align team priorities with company and customer goals, balancing short-term delivery, widely varying customer priorities, and long-term technical initiatives.
  4. Player-coach – While much of this role will be leading the team, you will also be expected to be a key driver on strategic product initiatives and customer engagements. The best managers derive credibility from being able to be hands-on when needed.
  5. Partner with sales on customer discovery calls (most often second calls, occasionally first calls for large accounts).

Skills

Required

  • AI/ML background
  • customer-facing communication skills
  • technical depth to scope solutions
  • script and prototype as needed

Nice to have

  • Experience running or supporting benchmarks for ML inference deployments
  • Familiarity with infrastructure tradeoffs relevant to inference performance and cost
  • Experience serving as a cross-functional technical lead for customer POCs
  • Professional Software engineering experience

What the JD emphasized

  • high performance, low latency AI applications
  • best-in-class performance, reliability, and cost efficiency
  • LLM deployment and inference optimization
  • low latency AI applications
  • GPU selection and latency versus throughput tuning

Other signals

  • customer-facing
  • LLM deployment
  • inference optimization
  • production environments