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.
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.