Senior Product Manager

About the role

HackerRank's goal is straightforward: to give companies a dependable way to evaluate whether a candidate can do the job. The previous model, which relied on test cases and code correctness, is becoming less reliable as candidates use AI assistants and produce work that requires deeper scrutiny.
The Evaluation team is rebuilding the underlying scoring system. This involves defining the new evaluation rubrics, determining how our models apply them consistently across thousands of candidates, working with enterprise clients to define good performance, and ensuring our methodology is fair and defensible.
Evaluation's trustworthiness is fundamental to HackerRank's business and its products. We are looking to hire a Senior Product Manager to own this crucial charter.

What you will do

• Own the evaluation product roadmap: what gets built, in what order, and why, with a clear point of view on what the product needs to become over the next 12 to 18 months.
• Define what "good evaluation" means in product terms: the rubrics, scoring dimensions, bias safeguards, and consistency checks that enterprise customers will stake hiring decisions on.
• Work directly with ML engineers and AI researchers to translate methodology into shipped product. You are the connective tissue between research clarity and engineering execution.
• Talk to enterprise customers regularly. Understand how Goldman Sachs defines a strong candidate differently from how a startup does, and make sure the product can serve both.
• Build the feedback loops that let the evaluation system improve: how we capture when customers disagree with a score, what signals we surface to the ML team, and how we close the loop.
• Define and own the metrics that tell us whether evaluation quality is actually improving, not just whether we shipped something.

Who you are

• You have 5 or more years of product experience, with meaningful time in a B2B or enterprise context where your work had to be defensible to sophisticated customers.
• You think rigorously about if the measures actually map to the outcomes. A PM who cannot tell the difference between a good metric and a vanity metric will not survive this role.
• You are comfortable with ambiguity in the problem definition. The answer to "what does good evaluation look like" does not exist yet. You need to want to find it, not wait for someone to hand it to you.
• You can hold your own in a technical conversation with ML engineers without needing to code. You understand enough about model behavior, evaluation pipelines, and data feedback loops to ask sharp questions.
• You communicate with precision. Crisp PRDs, sharp debate in design reviews, clear framing to customers and leadership. You do not hide behind vague language when the problem is hard.

Even better if you have

• Prior experience in assessment, ed-tech, talent tech, or any domain where you had to build products around subjectiv…