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. ## 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 AI 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
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here.
