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 RoleYou 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 Do1) 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 applySend 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.
Values- 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 Al forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing- Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
- Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
- Competitive compensation
- Flexible working hours
