Machine Learning Engineer

@ ReferEver

Role Overview

Mercor is driving a leading AI research initiative focused on benchmarking and enhancing model performance and training speed across a range of machine learning tasks. This project is ideal for Machine Learning Engineers (MLEs) with 5+ years of experience or Machine Learning PhDs interested in reproducible ML research and benchmarking AI model development.

Key Responsibilities

The role involves compiling external ML competitions into challenging tasks that mimic real-world responsibilities, drafting detailed executable natural language plans for MLE tasks, implementing those plans in Python within a provided Docker environment, and validating implementations against original plans with attention to discrepancies.

Ideal Qualifications

• 5+ years of applied machine learning experience or a PhD in machine learning or adjacent fields
• Strong Python engineering skills, particularly for model training and data handling
• Familiarity with Docker-based development environments
• Detail-oriented technical planning and code validation
• Experience with reproducibility and benchmarking in ML research (preferred)

More About the Opportunity

This is a remote, asynchronous role with flexible hours. Estimated commitment is 20 hours/week over an expected duration of 2+ months, with possibility for extension.

Compensation & Contract Terms

Up to $150/hour depending on region and experience, paid weekly via Stripe Connect, structured as an independent contractor engagement.

Application Process

Submit your resume, complete an AI interview (<10 minutes) and a short form revolving around your ML experience.

About Mercor

Mercor is a talent marketplace connecting top experts with leading AI labs and research organizations. Its investors include Benchmark, General Catalyst, Adam D’Angelo, Larry Summers, and Jack Dorsey. Thousands of professionals across domains have joined to work on frontier projects shaping the next era of AI.

Key skills/competency

• Machine Learning
• Python
• Docker
• Benchmarking
• Research
• Reproducibility
• Data Handling
• Technical Planning
• Model Training
• AI