Senior Machine Learning Engineer

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

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.

The agentic era is reshaping every layer of the hiring stack. These are some of the core problems you'll be working across, none of them fully solved.

• Chakra: Building an autonomous AI interviewer that conducts, adapts to, and evaluates technical interviews end to end.
• Integrity: Detecting fraud and suspicious behavior across multiple signal types. The ways candidates game assessments change frequently, and the models need to keep up.
• Evaluation: Measuring technical skill in a world where AI writes the code. The old proxies no longer hold and the new ones have not been defined yet.

Your focus will shift across these depending on where the highest-leverage work is at any given time.

• Design and ship production ML systems across Chakra, integrity, and evaluation domains.
• Own the full ML lifecycle: problem framing, data strategy, experimentation, deployment, and iteration.
• Build evaluation infrastructure and benchmarking pipelines that reliably measure model quality before and after deployment.
• Define the architecture and production bar for different signal categories from scratch. 
• Mentor and support junior ML engineers, helping shape their technical thinking and raise the quality bar across the team.
• Establish ML best practices for the team: monitoring, model feedback loops, and quality standards.

• 4+ years building and shipping ML systems that run in production at scale.
• 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.
• Proficient in Python, with practical experience building data pipelines and deploying models to production.

• 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.

• 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. 

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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