Senior AI Solutions Engineer

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: enterprise knowledge work (EKW)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

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

Compensation: OTE $260–320K (Senior) or $325–400K (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 enterprise knowledge work domain. Every major lab is racing to push the frontier on multi-step reasoning over enterprise 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 EKW 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 Enterprise Knowledge Workflow 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 EKW opportunities.
• Own the EKW 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 complex enterprise workflows (financial services, life sciences, legal, or similar) 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., GAIA, τ-bench, 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 EKW opportunities you cover is materially above the non-covered baseline; qualification bar codified.
• 180 days: a second Pre-Sales AI Solutions Engineer in the EKW domain hired behind you, ramping off your playbook.

Why Turing

• Work directly with the world's 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.

How to apply

Send a resume or 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. We read every submission.