Solution Architect

Baseten · Data AI · San Francisco, CA · EPD

Solution Architect role at Baseten, a company providing AI inference infrastructure. The role involves partnering with Sales and customers to understand business needs, design technical solutions, run technical discovery, and guide deployments and proofs of value. Responsibilities include customer discovery calls, technical scoping, leading demos, owning benchmarking and repeatable deployments across various AI modalities, advising on infrastructure tradeoffs, and driving POC execution. Requires an AI/ML background, strong customer-facing communication, and technical depth to scope solutions.

What you'd actually do

  1. Partner with Sales on customer discovery calls (most often second calls, occasionally first calls for large accounts).
  2. Lead demos and technical scoping to align on success criteria, architecture, and deployment approach.
  3. Own benchmarking and repeatable deployments, including:
  4. Handling standard deployment patterns and configurations across many modalities – LLMs, embeddings, image and video generation, VoiceAI, etc.
  5. Advising on tradeoffs like H100s vs B200s and latency-optimized vs throughput-optimized setups.
  6. Driving consistent “playbook” style deployments for common models and use cases.
  7. Become a power user of different runtimes such as vllm, sglang, and TRT-LMM and all the common configurations and tradeoffs between them
  8. Drive POC and project execution, including:
  9. Scoping POCs and keeping stakeholders aligned on timeline, deliverables, and next steps.
  10. Acting as the “ringleader” or project manager for POCs.
  11. Pulling in Forward Deployed Engineering (FDE) support when deeper or more complex technical work is needed.

Skills

Required

  • AI/ML background
  • Customer-facing communication skills
  • Technical depth to scope solutions
  • Scripting and prototyping

Nice to have

  • Experience running or supporting benchmarks for ML inference deployments.
  • Familiarity with infrastructure tradeoffs relevant to inference performance and cost (for example GPU selection and latency versus throughput tuning).
  • Experience serving as a cross-functional technical lead for customer POCs, including coordination across Sales and Engineering.

What the JD emphasized

  • AI/ML background and the ability to credibly discuss AI/ML topics with technical stakeholders.
  • Technical depth to scope solutions, without needing to write production code.
  • Ability to script and prototype as needed, including comfort “vibe coding” to move quickly in technical workflows.

Other signals

  • customer-facing technical professional
  • translate business needs into technical solutions
  • guide repeatable deployments
  • AI at scale
  • power user of different runtimes