HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next.

Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills.

People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards.

About the role

How do you know if a candidate is doing well when there is no right answer?
For twenty years, code evaluation was deterministic. A solution passed a test case or it did not. That world is over. Candidates today work alongside AI assistants, engage in multi-turn conversations, and produce outputs that blend human and machine reasoning. The evaluation signal is now qualitative, contextual, and deeply subjective.
What Goldman Sachs considers a strong candidate looks different from what a Series B startup considers one. What looks like a correct answer in a Python interview looks different when a transcript is in the mix. And without a rigorous methodology for defining "good," any evaluation system we build risks embedding bias we cannot see and cannot defend.
This role exists to solve that. Not as a support function. As the foundational research layer that everything else depends on.

What you will do

Design and execute research studies to define what "good performance" means across different job contexts, industries, and candidate types.
Build the datasets, annotation frameworks, and ground truth labels that ML models on the Evaluation team learn from.
Own the bias audit process: identify where our evaluation systems treat groups differently, understand why, and recommend what to change.
Conduct customer and industry interviews to understand how enterprise clients define skilled performance, then translate those signals into measurable constructs.
Write and maintain documentation on research methodology that product, ML, and commercial teams can reference and trust.
Stay current on relevant literature in educational measurement, psychometrics, algorithmic fairness, and LLM evaluation, and bring applicable ideas into the work.

Who you are

You have a Master's or PhD in a research-intensive discipline: cognitive science, computational social science, NLP, educational measurement, human-computer interaction, or a closely adjacent field.
You know how to design a methodology from scratch. You do not need a playbook. You can structure a research question, identify what data would answer it, and execute through to a defensible conclusion.
You are digitally self-sufficient. You can write your own scripts, scrape data, run your own analyses, and work without a dedicated data engineer holding your hand.
You think carefully about what fairness actually means in a measurement context, not just as a talking point but as a design constraint.
You communicate findings to non-researchers without oversimplifying. You know how to make a methodology legible to a PM, a customer, and an engineer.

Even better if you have

Familiarity with psychometric frameworks, IRT, or educational assessment standards.
Published work or thesis research touching on algorithmic fairness, evaluation design, or human judgment in AI systems.
Experience running user research or qualitative studies alongside quantitative modeling.
Prior exposure to B2B or enterprise product environments where research outputs feed directly into shipped product.

You will thrive here if

You are energized by the fact that there is no prior art for this problem.
You find defining the measurement framework as intellectually interesting as building the model.
You are the kind of researcher who talks to customers, gets their hands in messy data, and does not wait to be assigned a hypothesis. You want your research to ship, not just get published.

Want to learn more about HackerRank? Check out HackerRank.com to explore our products, solutions and resources, and dive into our story and mission here.

HackerRank is a proud equal employment opportunity and affirmative action employer. We provide equal opportunity to everyone for employment based on individual performance and qualification. We never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

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