AI Architect

<h2><strong>About the role</strong></h2> <p>We’re hiring an AI Architect to sit at the intersection of <strong>frontier AI research, product, and go-to-market.</strong> You’ll partner closely with ML teams in high-stakes meetings, scope and pitch solutions to top AI labs, and translate research needs (post-training, evals, alignment) into clear product roadmaps and measurable outcomes. You’ll drive end-to-end delivery—partnering with AI research teams and core customers to scope, pilot, and iterate on frontier model improvements—while coordinating with engineering, ops, and finance to translate cutting-edge research into deployable, high-impact solutions.</p> <h2><strong>What you’ll do</strong></h2> <ul> <li><strong>Translate research → product:</strong> work with client side researchers on post-training, evals, safety/alignment and build the primitives, data, and tooling they need.</li> <li><strong>Partner deeply with core customers and frontier labs:</strong> work hands-on with leading AI teams and frontier research labs to tackle hard, open-ended technical problems related to frontier model improvement, performance, and deployment.<br><strong>Shape and propose model improvement work:</strong> translate customer and research objectives into clear, technically rigorous proposals—scoping post-training, evaluation, and safety work into well-defined statements of work and execution plans.</li> <li><strong>Translate research into production impact:</strong> collaborate with customer-side researchers on post-training, evaluations, and alignment, and help design the data, primitives, and tooling required to improve frontier models in practice.</li> <li><strong>Own the end-to-end lifecycle:</strong> lead discovery, write crisp PRDs and technical specs, prioritize trade-offs, run experiments, ship initial solutions, and scale successful pilots into durable, repeatable offerings.</li> <li><strong>Lead complex, high-stakes engagements:</strong> independently run technical working sessions with senior customer stakeholders; define success metrics; surface risks early; and drive programs to measurable outcomes.</li> <li><strong>Partner across Scale:</strong> collaborate closely with research (agents, browser/SWE agents), platform, operations, security, and finance to deliver reliable, production-grade results for demanding customers.</li> <li><strong>Build evaluation rigor at the frontier:</strong> design and stand up robust evaluation frameworks (e.g., RLVR, benchmarks), close the loop with data quality and feedback, and share learnings that elevate technical execution across accounts.</li> </ul> <h2><strong>You have</strong></h2> <ul> <li>Deep technical background in applied AI/ML: 5–10+ years in research, engineering, solutions engineering, or technical product roles working on LLMs or multimodal systems, ideally in high-stakes, customer-facing environments.</li> <li>Hands-on experience with model improvement workflows: demonstrated experience with post-training techniques, evaluation design, benchmarking, and model quality iteration.</li> <li>Ability to work on hard, ambiguous technical problems: proven track record of partnering directly with advanced customers or research teams to scope, reason through, and execute on deep technical challenges involving frontier models.</li> <li>Strong technical fluency: you can read papers, interrogate metrics, write or review complex Python/SQL for analysis, and reason about model-data trade-offs.</li> <li>Executive presence with world-class researchers and enterprise leaders; excellent writing and storytelling.</li> <li>Bias to action: ... (truncated, view full listing at source)