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.

We are looking for an AI Data Operations Manager to own and run our end-to-end data creation and maintenance pipeline, from sourcing and managing vendors to coordinating annotation workflows and ensuring the data our models train on meets the highest quality standards.
This is a cross-functional operations role that sits at the intersection of ML, product, and operations management. You will be the connective tissue between our machine learning engineers, internal annotators, and external vendors, translating technical requirements into actionable data creation workflows and ensuring every batch of data that reaches our models is accurate, well-annotated, and fit for purpose. Critically, you will also drive automation across the pipeline, designing and deploying AI agents that eliminate manual bottlenecks and make the entire operation smarter over time.

• Own the full vendor lifecycle, from sourcing and onboarding to quality management and relationship building at scale.
• Design and deploy agentic workflows that automate multi-step processes including vendor briefing, annotation verification, and batch tracking.
• Translate model evaluation signals (precision gaps, recall failures, edge case blind spots) into targeted data creation cycles.
• Partner with MLEs and PMs to stay ahead of model data needs, and close the loop between evaluation findings and pipeline improvements.
• Maintain clear, living documentation across data batches, annotation guidelines, and vendor SOPs.

• 3+ years in data operations, annotation management, or vendor management in an AI/ML or tech environment.
• AI-fluent: you use AI tools naturally, think in terms of what can be automated, and can hold credible conversations with both technical and non-technical stakeholders about model behavior, prompt design, and agent architecture. 
• Hands-on experience building or working with AI agents. Organized and detail-oriented, comfortable managing multiple vendors, deadlines, and pipelines simultaneously.
• Familiar with ML evaluation basics (precision, recall, edge cases) and can engage meaningfully with engineers on data quality.

• Prior experience working with NLP or AI training data pipelines.
• Familiarity with annotation tools (e.g. CVAT, Label Studio, Scale AI, Surge AI, or similar).
• Experience working in a startup or high-growth environment where processes are still being built.

• Get frustrated when things are manual that don't need to be, and you do something about it.
• Can translate messy technical requirements into clear, actionable briefs without losing anything in translation.
• Treat data quality as a product, not a checkbox.
• Are comfortable operating in the gap between engineering and operations, and you actually enjoy being there.

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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