Data Engineer
Mercor
Job Overview
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Job Description
About The Job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position Overview
Data Engineer (Contract, Full-time, Onsite). You will manage data labeling workflows, maintain large-scale data processing pipelines using machine learning models and LLMs, ensure dataset governance with focus on privacy compliance, collaborate with engineers to optimize model training, and scale evaluation protocols.
Role Responsibilities
- Manage data labeling workflows including UI and dataset extraction.
- Maintain efficient data pipelines for processing billions of images.
- Ensure data governance, access control, retention, and privacy.
- Collaborate with engineers for model gap identification and preparation.
- Audit and correct human-labeled data for model performance.
Qualifications
Must-Have: Basic knowledge of Python, SQL, computer vision, generative models, data ETL workflows; Associate's degree or equivalent in STEM; strong communication, problem-solving, and detail-oriented skills.
Preferred: Industrial software development experience, human-computer interaction research, previous experience at major tech companies.
Compensation & Legal
Hourly contractor with competitive compensation between $70–$85/hour. Eligible candidates will be considered irrespective of arrest or conviction records in compliance with the California Fair Chance Act.
Application Process
Complete the application in 20–30 minutes by uploading your resume, completing an AI interview based on your resume, and submitting the form. For process details, visit this page. For support, contact support@mercor.com.
Key skills/competency
- Python
- SQL
- ETL
- Machine Learning
- Computer Vision
- Data Labeling
- LLMs
- Data Governance
- Data Pipelines
- Compliance
How to Get Hired at Mercor
- Research Mercor's culture: Learn about their AI and funding background.
- Customize your resume: Highlight Python, SQL, and ETL skills.
- Prepare technical examples: Showcase large-scale data projects.
- Practice behavioral responses: Demonstrate teamwork and problem-solving.
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