Member of Technical Staff, Enterprise Evals Platform

2 нед. назад

220k–425k USD / yearUSALeadOnsite

python

Backend developer role to build the evaluation platform supporting AI enterprise agents at .

Обязанности

Enterprise agents are complex systems, and they only pay off when their work is reliable and economically viable. Evaluation is how you get both: checking correctness is the obvious case, and routing is the subtler one, since choosing a model against cost, latency, and quality requires quality to be measurable at all. Knowing where the bar sits is the hard part. You decompose real work, take the standard from the practitioners who hold it, and encode it so an agent cannot shortcut it.
You will apply what has learned building benchmarks with domain experts, and devise new methods, so that evals and rubrics keep improving and so do the agents measured against them.
That work only scales with a platform behind it. This is a platform engineering role with good depth of understanding in evals. You will build the verifiers, the environments agents are measured in, and the grading infrastructure that runs at scale, abstracted across customers, domains, and tasks so that every run becomes evidence the next agent inherits instead of starting over.
Read more about how we think about this: Agent Eval Systems
Define golden sets: decompose real tasks and encode the expert quality bar.
Build verifiers over agent trajectories and outputs, calibrated and hard to game.
Build the eval platform that runs offline environments, task suites, and grading at scale.
Run loss analysis over production trajectories and turn failure modes into regression tests.
Run the optimization loop across models, prompts, skills, and harnesses.
Own the rollout gates that decide whether an agent change ships.
Partner with the Enterprise Platform team and the Applied AI engineers embedded with customers.

Требования

Professional, academic, or research experience in agent engineering and evaluation, including how agent runtimes and harnesses produce a trajectory and where it fails.
Experience building evaluation suites for LLM or agent systems, and familiarity with how benchmarks such as terminal-bench, tau-bench, and APEX are constructed and where they get gamed.
Judgment about task and rubric design: turning a fuzzy notion of quality into something measurable, with agent or model improvements to show for it.
Strong software engineering fundamentals, and the ability to work independently on ambiguous, loosely specified problems.
Bonus: experience with Harbor environments and RL environments.

Условия

Up to $15k relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Generous equity grant vested over 4 years
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance

Другое

's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. 's APEX benchmark family measures AI's real-world impact on professional work. Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
Impact: No agent reaches an enterprise customer without clearing the bar you set.
Learning: Eval work spanning frontier model measurement and live enterprise deployments, on production trajectories few teams get to see.
Growth: Research and systems in one role, with fast paths to owning the eval system end to end.