Senior AI Solutions Engineer STEM

Based in San Francisco California Turing is the worlds 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 worlds top innovators Turings leadership team includes AI technologists from Meta Google Microsoft Apple Amazon McKinsey Bain Stanford Caltech and MIT. Learn more at

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.

Reports to: CRO (until VP Field Engineering is hired)

Compensation: OTE $ (Senior) or $ (Staff) 75/25 base/variable split Equity

You will be the first technical partner to Turings 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.

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 Turings 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 labs 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.

• 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.

• 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.

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.

Why Turing

• Work directly with the worlds leading AI labs at the cutting edge of post-training evaluation and agentic AI research.
• Real impact on the path to AGI: the datasets evaluations and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI not to procurement.

Send a CV and a short note on a technical artifact you built ideally something customer-facing evaluation-adjacent or that demonstrates how you think about technical scoping to We read every submission.

• We are client first: We put our clients at the center of everything we do because their success is the ultimate measure of our value.

• We work at Start-Up Speed: We move fast stay agile and favor action because momentum is the foundation of perfection

• We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.