Senior AI Solutions Engineer - Software Engineering

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. Learn more at www.turing.com Department : Field Engineering — Pre-Sales (Founding) Level: Senior (Staff level considered for exceptional candidates) Domain: Software Engineering (SWE) Location: Strong preference for SF Bay Area but will consider Seattle and NYC. Reports to: CRO (until VP, Field Engineering is hired) Compensation: OTE $260,000 – 320,000 (Senior) or $325,000 – 400,000 (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 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 1) 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. 2) 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. 3) 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. 4) 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. 5) 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. 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. 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 — b