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 software engineering domain. Every major lab is racing to push the frontier on code generation, agentic software engineering, and SWE evaluation. 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 Field Engineering founding 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 conversation on every qualified SWE opportunity. Partner with Research Partners to run the technical track with AI researchers and research leads. Understand what they're training, what they're evaluating, where their pipeline breaks, and what a Turing-built artifact looks like in practice. Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.
- Solution architecture — translate lab needs into scoped Turing deliverables. Map capability goals to Turing's offering shapes: custom human expert data, off-the-shelf datasets, and managed talent. Author technical proposals that AI researchers accept and the Production Engineering team can execute without a rewrite.
- Prototyping and demo-building — prove the approach before contract. Build sample eval tasks, reference dataset slices, graded trajectories, and working agentic scaffolds. Expect to write real code, not mock-ups. The demo has to run.
- POC ownership — take paid pilots from kick-off to scale-up decision. Design the measurement plan, define success criteria, own the cadence. The outcome you are measured on: POC converts to production contract.
- R&D interface — be the pre-sales channel between GTM and R&D for SWE. Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring. Maintain a predictable 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. Own the SWE section of the Field Engineering knowledge base.
Who We're Looking For
- 5+ years in software or ML engineering, with meaningful production experience on code-generation, code-understanding, or developer-tooling systems.
- Hands-on fluency in Python and modern LLM tooling; comfort reading and writing across at least one other major language (TypeScript, Go, Rust, or Java).
- Experience designing or working with evaluations for code models — benchmarks, rubric design, grader reliability, eval construction.
- Experience with large codebases, agentic SWE systems, or developer-facing AI products.
- A high written communication bar: you can produce a scoping document that a frontier lab engineer 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, AI infrastructure company, or developer-tools company shipping to AI customers.
- Experience with SWE benchmark construction (e.g., SWE-bench, LiveCodeBench, or equivalents).
- Background in pre-sales, solutions architecture, or technical consulting.
What success looks like
- 30 days: first FE-led POC signed; software engineering domain discovery playbook v1 published; three demo artifacts in the library.
- 60 days: win rate on SWE opportunities you cover is materially above the non-covered baseline; qualification bar codified; R&D and Field Engineering interface running on a predictable cadence.
- 180 days: a second Pre-Sales AI Solutions Engineer in the SWE domain hired behind you, ramping off your playbook. You spend less than 30% of your time as a solo contributor and more than 70% multiplying through the function.
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.