Senior AI Solutions Engineer - STEM

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the STEM domain. Every major lab is racing to push the frontier on multi-step reasoning over STEM data, tool use, long-horizon task completion, and evaluation that reflects real work. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

What You'll Do

1. Technical discovery — lead the technical track on every qualified STEM opportunity
   • Partner with Research Partners to run the technical conversation with lab researchers and engineers.
   • Understand what agentic capability the lab is trying to unlock, what "good" looks like, and what evaluations a post-training team would actually trust.
   • Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.
2. Solution architecture — translate capability goals into scoped Turing deliverables
   • Map research goals to Turing's offering shapes: agentic trajectories, rubric-graded reasoning tasks, tool-use evaluations, and domain-specialist-built datasets.
   • Author technical proposals that frontier lab research leads accept and the Production Engineering team can execute without a rewrite.
3. Prototyping and demo-building — prove the approach before contract
   • Build reference agent loops, sample multi-step evaluations, and graded trajectories that demonstrate quality before contract signature.
   • The demo has to run. Expect to write real code.
4. POC ownership — take paid pilots from kick-off to scale-up decision
   • Design a measurement plan the lab's research team will actually read and act on.
   • Define success criteria, own the cadence, convert POC to production contract.
5. R&D interface — channel GTM-to-R&D asks for STEM opportunities
   • Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
   • Maintain a collaboration cadence that R&D teams trust.
6. Playbook building — codify what works so future hires scale faster than you did
   • Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns for STEM opportunities.
   • Own the STEM section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in applied AI, data engineering, or ML engineering, with meaningful work on agentic systems, RAG, tool use, or enterprise-knowledge LLM applications.
• Strong Python fluency and production experience with LLM orchestration frameworks (LangGraph, LlamaIndex, DSPy, or equivalents).
• Experience designing evaluations for multi-step reasoning or agentic systems — rubric design, trajectory grading, measurement beyond single-turn accuracy.
• Exposure to frontier STEM workflows (biology, chemistry, physics, medical, engineering, mathematics) and the data and permission realities inside them.
• A high written communication bar: you can produce a scoping document that a frontier lab research lead accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, an AI startup building agentic products, or an enterprise AI team shipping to production.
• Experience with agentic or reasoning benchmarks (e.g., HLE, GPQA, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; enterprise knowledge work domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on STEM opportunities you cover is materially above the non-covered baseline; qualification bar codified.
• 180 days: a second Pre-Sales AI Solutions Engineer in the STEM domain hired behind you, ramping off your playbook.