Senior AI Solutions Engineer, Software Engineering

• →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.

• 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.

• 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.

• Design the measurement plan, define success criteria, own the cadence.
• The outcome you are measured on: POC converts to production contract.

• 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.

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns.
• Own the SWE section of the Field Engineering knowledge base.

• 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.

• 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.

• 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.

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.

• 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.