AI Architect Job at Scale (San Francisco)

Job Description

We’re hiring an AI Architect to sit at the intersection of frontier AI research, product, and go-to-market. 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.

Job Responsibility

Translate research → product: work with client side researchers on post-training, evals, safety/alignment and build the primitives, data, and tooling they need
Partner deeply with core customers and frontier labs: 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
Shape and propose model improvement work: 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
Translate research into production impact: 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
Own the end-to-end lifecycle: lead discovery, write crisp PRDs and technical specs, prioritize trade-offs, run experiments, ship initial solutions, and scale successful pilots into durable, repeatable offerings
Lead complex, high-stakes engagements: independently run technical working sessions with senior customer stakeholders
define success metrics
surface risks early
and drive programs to measurable outcomes
Partner across Scale: collaborate closely with research (agents, browser/SWE agents), platform, operations, security, and finance to deliver reliable, production-grade results for demanding customers
Build evaluation rigor at the frontier: 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.

Requirements

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
Hands-on experience with model improvement workflows: demonstrated experience with post-training techniques, evaluation design, benchmarking, and model quality iteration
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
Strong technical fluency: you can read papers, interrogate metrics, write or review complex Python/SQL for analysis, and reason about model-data trade-offs
Executive presence with world-class researchers and enterprise leaders
excellent writing and storytelling
Bias to action: you ship, learn, and iterate.

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend
equity based compensation.

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