Senior Machine Learning Engineer

About the role

Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The ML systems that power this platform are not auxiliary features. They are the product.

Open Problem

The state of the art is not good enough. That is not a gap in execution. It is a research frontier.We are building ML systems across three unsolved problem areas.

Integrity: detecting fraud and anomalous behavior across heterogeneous, adversarially non-stationary signals.
Evaluation: measuring technical skill in a world where AI writes the code, and the old proxies no longer hold.
Chakra: an autonomous AI interviewer that conducts, adapts, and evaluates technical interviews end to end.

Each of these problems is hard in a different way. None of them have clean existing solutions to fine-tune from. The person we are hiring will define what good looks like, not inherit it.

What you will do

• Design and ship production ML systems across integrity, evaluation, and autonomous interview domains.
• Own the full ML lifecycle: problem framing, data strategy, experimentation, deployment, and iteration.
• Build evaluation infrastructure and benchmarking pipelines that give real confidence in what ships.
• Define the architecture and production bar for new signal categories from scratch.
• Establish ML best practices for the team: monitoring, model feedback loops, and quality standards.

Who you are

• 4+ years building and shipping ML systems that run in production and hold up under pressure.
• Systems thinking comes naturally. Model accuracy, data pipelines, serving infrastructure, and customer outcomes are one problem, not four.
• Evaluation methodology matters as much as model performance. A metric measured wrong is worse than no metric.
• Python is your primary language and the full stack from data pipelines to model serving is comfortable territory.
• The bar you hold yourself to is higher than the one the market has set.

Even better if you have

• Experience with multimodal systems: vision, NLP, audio, or behavioral signal pipelines.
• LLM experience: fine-tuning, RLHF, or multi-turn agentic systems.
• Background in adversarial ML, fraud detection, or anomaly detection.
• Publications or open-source contributions in detection, robustness, or evaluation methodology.

You will thrive here if

• Messy, undefined problems are more interesting to you than optimizing within clean ones.
• Ambiguity energizes you, especially when the right framing is itself part of the work.
• Direct access to leadership, fast feedback loops, and genuinely unsolved problems is what you are looking for.
• Defining what a system should be is more compelling than maintaining what already exists.