Job description

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy. The Role We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them. We are looking for an engineer to build the infrastructure and tooling that make high-quality AI evaluation possible at Block's scale. You will help teams understand whether a model or product change is actually better, whether a result is statistically meaningful, and whether offline evaluation is predicting what happens with real users. Our evaluation approach combines offline evals that encode our definition of a good response, online evals that show how people actually respond, and a feedback loop that keeps the two converging. Your work will turn that approach into systems that product teams can use quickly, reliably, and with confidence. This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster. You Will Build an execution engine that can score candidate versions against task sets in minutes, not hours. Create task set tooling that samples from production logs and validates tasks before they are admitted into an evaluation set. Build grader infrastructure across ground truth checks, rubrics, and LLM-as-judge approaches. Develop tooling that helps human reviewers calibrate judges, measure judge-to-human agreement, and monitor drift over time. Build leaderboards and reporting systems that include sample size, confidence intervals, and run-to-run variance, so teams can distinguish real improvements from noise. Support in-product side-by-side serving, feedback capture, and implicit signal extraction from real conversations. Build the loop that compares offline scores with online outcomes, identifies eval sets that have stopped predicting reality, and helps teams improve them. Partner with product, engineering, data, and ML teams to make evaluation workflows fast enough and trustworthy enough to become part of everyday development. You Have Experience building production platforms or infrastructure, including distributed batch execution, data pipelines, or systems that process production logs. Strong statistical literacy, including comfort with confidence intervals, variance, power, and multiple comparisons. The judgment to identify when a result is meaningful and when it is noise. Experience evaluating LLM or ML systems, or deep systems engineering experience with a strong interest in AI evaluation. Product instinct for internal tools. You understand that leaderboards, annotation tools, and workflows only matter if teams actually use them. A bias toward building reliable, observable systems that other engineers can trust. Strong collaboration skills and the ability to work across ambiguous product, data, and engineering problems. Success in your first year Product teams can stand up credible evals for new AI surfaces in days. Eval results gate CI and run quickly enough that engineers do not route around them. Judge-to-human agreement is measured, published, and monitored for drift. Launch decisions are not made on results that are within statistical noise. At least one case is documented where online reality disagreed with offline scores, and the evaluation was improved as a result. Why this matters AI product development moves quickly, but speed only helps when teams can trust the signal they are using to make decisions. This role will build the systems that make those signals faster, more accurate, and more actionable. Your work will directly influence how Block evaluates, improves, and ships AI products. Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future. To find a location’s zone designation, please refer to this resource . If a location of interest is not listed, please speak with a recruiter for additional information. Zone A: $263,600 — $395,400 USD Zone B: $263,600 — $395,400 USD Zone C: $263,600 — $395,400 USD Zone D: $263,600 — $395,400 USD Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone. Privacy Policy

Requirements

Must-haves

We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them.
This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster.
Build an execution engine that can score candidate versions against task sets in minutes, not hours.
Create task set tooling that samples from production logs and validates tasks before they are admitted into an evaluation set.
Build grader infrastructure across ground truth checks, rubrics, and LLM-as-judge approaches.
Develop tooling that helps human reviewers calibrate judges, measure judge-to-human agreement, and monitor drift over time.
Build leaderboards and reporting systems that include sample size, confidence intervals, and run-to-run variance, so teams can distinguish real improvements from noise.
Support in-product side-by-side serving, feedback capture, and implicit signal extraction from real conversations.
Build the loop that compares offline scores with online outcomes, identifies eval sets that have stopped predicting reality, and helps teams improve them.
Partner with product, engineering, data, and ML teams to make evaluation workflows fast enough and trustworthy enough to become part of everyday development.
Experience building production platforms or infrastructure, including distributed batch execution, data pipelines, or systems that process production logs.
Strong statistical literacy, including comfort with confidence intervals, variance, power, and multiple comparisons.
The judgment to identify when a result is meaningful and when it is noise.
Experience evaluating LLM or ML systems, or deep systems engineering experience with a strong interest in AI evaluation.
Product instinct for internal tools. You understand that leaderboards, annotation tools, and workflows only matter if teams actually use them.
A bias toward building reliable, observable systems that other engineers can trust.
Strong collaboration skills and the ability to work across ambiguous product, data, and engineering problems.
Success in your first year
Product teams can stand up credible evals for new AI surfaces in days.
Eval results gate CI and run quickly enough that engineers do not route around them.
Judge-to-human agreement is measured, published, and monitored for drift.
Launch decisions are not made on results that are within statistical noise.
At least one case is documented where online reality disagreed with offline scores, and the evaluation was improved as a result.
Why this matters

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmml

Platform: greenhouse
Effort: quick