5-7 years of experience in evaluation processes that drive significant decisions
Deep understanding of evaluation philosophy, including validity and contamination
Experience with consequential evaluations in video, robotics, or multimodal domains
Ability to differentiate between superficial metrics and meaningful evaluations
Strong analytical skills to assess model performance and failure patterns
Responsibilities
Design and execute benchmarks for video and world models under consistent protocols
Ensure honest comparisons through rigorous checks for contamination and leakage
Develop an analytics framework to assess model performance and dataset scaling
Collaborate with research and quality teams to enhance data collection and evaluation processes
Drive impactful evaluations that influence strategic decisions
Create scalable processes to improve evaluation quality and repeatability
Benefits
Competitive pay
Comprehensive medical, dental, and vision coverage
$500 monthly credit for waiving medical benefits
$2,000 monthly housing subsidy for local employees
Relocation support for moves to San Francisco or Shenzhen
Wellness benefits for fitness and mental health
Daily meals provided in the office
Unlimited compute budget with ROI justification
Unlimited access to Codex and Claude credits
Travel opportunities
Full Job Description
Job Summary
We're hiring an Evals Lead to benchmark video and world-model capabilities with and without our data. You need to deeply understand the philosophy of evals: what an eval is allowed to claim, contamination, leakage, construct validity, and whether a number actually corresponds to a capability. More practically, you should have pushed consequential evals before - evals that changed what a lab trained, shipped, or collected, not a weekend leaderboard. Key Responsibilities
Design and run benchmarks for video and world models, with Build data and without it, under the same protocol
Make the comparison honest: held-out tasks, contamination and leakage checks, no cooking the numbers
Build the analytics layer: model performance, failure patterns, and whether scaling the dataset moves capability - and on which axes
Work with research and dataset/quality so collection and evals inform each other
Push evals that are consequential enough that people change plans when the number moves
Design processes that increase evaluation quality, repeatability, and scale as we add tasks and countries
You may be a good fit if you have (Must-have qualifications)
You have shipped or driven evals that mattered: they changed training, hiring, product, or data decisions
You understand eval philosophy well enough to argue about validity, not only to plot a curve
Ideally video, robotics, world models, or multimodal, but the bar is consequential evals more than a specific domain
You will not confuse a pretty dashboard with an eval that is allowed to decide things
Strong candidates may also have experience with (Nice-to-have qualifications)
Video, robotics, world models, or multimodal evals
You have designed evals used in a paper, a product launch, or a data decision
Background in construct validity, contamination, or leakage
Exposure to evaluation operations: throughput, failure taxonomy, repeatability
Benefits
Competitive pay
Medical, dental, and vision packages with generous premium coverage
$500 per month credit for waiving medical benefits
Housing subsidy of $2k per month for those living within walking distance of the office
Relocation support for those moving to San Francisco (Financial District) or Shenzhen (Nanshan)
Various wellness benefits covering fitness, mental health, and more
Daily lunch and dinner in our office
Unlimited compute budget subject to ROI justification
Unlimited Codex and Claude credits
Travel
How we're different
Build believes in the Bitter Lesson. By taking a general approach of learning from humans, our addressable market is all physical labor.
We are a fully in-person team in San Francisco (Financial District) and Shenzhen (Nanshan), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.
