Fast Facts

Join HackerRank as an AI Data Operations Manager, responsible for managing the end-to-end data pipeline for machine learning models and ensuring high-quality dataset delivery.

Responsibilities: Key responsibilities include requirement gathering, managing labelers and vendors, ensuring process quality control, and delivering production-ready datasets.

Skills: Required skills include experience in data operations, knowledge of ML/LLM data formats, project management abilities, and proficiency in SQL or Python for metrics tracking.

Qualifications: Preferred qualifications include 4+ years in data operations for ML projects, experience with quality-control processes, and familiarity with software engineering datasets and security frameworks.

Location: The role is hybrid based in the San Francisco Bay Area, California, and does not require travel.

Compensation: $130000 - $150000 / Annually

About the team:

HackerRank’s Machine Learning team is working on the cutting edge of AI. We’re actively researching and building solutions for a number of exciting initiatives, including plagiarism detection within our Integrity Workstream, how LLMs perform across the SDLC via our ASTRA evaluation harness and benchmark, the types of datasets that can further improve LLMs on software engineering tasks, and finally how far we can take software development agents with our rich datasets.

About the role:

The AI Data Operations Manager owns the end-to-end pipeline that turns raw content into clean, well-labeled datasets for training and evaluating machine-learning models and LLMs. You’ll gather data requirements from ML researchers and product teams, work with our Content team to coordinate a global network of subject-matter-expert (SME) labelers, and deliver production-ready datasets to internal stakeholders and customers.

What you'll do:

Requirement gathering – Translate model or product needs into clear data specs, timelines, and success metrics.
Labeler & vendor management – Source, onboard, and coach SME labelers or third-party vendors; track throughput and cost.
Process & quality control – Design scalable workflows, run sampling audits, and drive continual quality improvements (e.g., inter-annotator agreement targets).
Data delivery & documentation – Package datasets with schemas, metadata, and usage guidelines; ensure security and licensing compliance.

You will thrive in this role if you:

Understand the importance of operational excellence, strong organizational skills, and the ability to drive multiple stakeholders who are all going to be busy and distracted.
Can handle ambiguity and drive clarity.
Can strike the right balance between customer requests and internal requests.
Are able to quickly identify and troubleshoot bottlenecks in complex and human-driven processes at scale.

What you bring:

4+ years in data operations, program management, or content operations for ML, NLP, and AI projects.
Hands-on experience shipping labeled datasets at scale and running quality-control processes, determining the best balance between building and buying tools to scale.
Working knowledge of ML/LLM data formats and annotation platforms (e.g., Labelbox, Scale, custom tools).
Strong project-management skills and proficiency with SQL or Python/pandas for metric tracking.
Excellent written and verbal communication; able to work with engineers, researchers, and customers.

Bonus Skills:

Experience with coding challenges or software engineering datasets.
Familiarity with privacy and security frameworks (GDPR, SOC 2).
Record of managing distributed or crowdsourced labeling teams.

Current base salary range: ($130,000 to $150,000). The exact salary may vary based on skills, experience, location, market ranges, and other compensation offered. The salary range does not include other compensation components, commissions (for sales-related roles), bonuses, or benefits that you may be eligible for. Salary may be adjusted based on business needs.

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AI Data Operations Manager