Turing - Senior AI Solutions Engineer
Requirements
• 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 frontier STEM workflows (biology, chemistry, physics, medical, engineering, mathematics) 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 • 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., HLE, GPQA, 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. • 30 days: • 60 days: win rate on STEM opportunities you cover is materially above the non-covered baseline; qualification bar codified. • 60 days: • 180 days: a second Pre-Sales AI Solutions Engineer in the STEM domain hired behind you, ramping off your playbook. • 180 days:
Responsibilities
• 1) Technical discovery — lead the technical track on every qualified STEM 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 STEM 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 STEM opportunities. • Own the STEM section of the Field Engineering knowledge base.
Benefits
• 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. • Flexible working hours
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