Senior AI Solutions Engineer - STEM

About Turing Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.

Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. com Department : Field Engineering — Pre-Sales (Founding) Level: Senior (Staff level considered for exceptional candidates) Domain: STEM Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Compensation

: OTE $260,000–320,000 (Senior) or $325,000–400,000 (Staff) · 75/25 base/variable split · Equity 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. This is a founding Field Engineering role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

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.