ML Engineer (Remote)

> 📍 Location: Remote (Work from Anywhere)

> 💼 Job Type: Part-Time

> 💰 Payout: Competitive, based on experience

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Role Overview

We are hiring on behalf of a global leader in the Technology, Information, and Internet industry for ML Engineers to join an MLE Bench project on a part-time basis.

This role involves hands-on work with production-grade ML codebases, model training, evaluation pipelines, and deployment-oriented workflows to help assess and improve the capabilities of frontier AI systems. The ideal candidate bridges research and engineering, working deeply with models, data, and infrastructure in realistic ML environments alongside top AI researchers and engineers.

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Key Responsibilities

• Benchmarking & Evaluation: Work with real-world ML codebases to support MLE Bench–style evaluation tasks.
• Pipeline Management: Build, execute, and modify model training, evaluation, and inference pipelines.
• Data Preparation: Prepare datasets, features, and evaluation metrics for ML benchmarking and validation.
• Codebase Refactoring & Optimization: Debug, refactor, and improve production-like ML systems for correctness, stability, and performance.
• Model Diagnostics: Evaluate model behavior, failure modes, and edge cases relevant to benchmark tasks.

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Required Skills & Qualifications

• Experience: 3+ years of overall experience as a Machine Learning Engineer or ML-focused Software Engineer.
• Programming: Strong proficiency in Python for machine learning and data workflows.
• ML Pipelines: Hands-on experience building and modifying model training, evaluation, and inference pipelines.
• Core Fundamentals: Solid understanding of machine learning fundamentals (supervised/unsupervised learning, metrics, optimization).
• Frameworks: Hands-on experience with major ML frameworks such as PyTorch or TensorFlow.

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About the Client & Hiring Process

• Opportunity: Contribute directly to the development and evaluation of cutting-edge frontier AI systems.
• Equal Opportunity Employer: Hiring decisions are based solely on technical ability, demonstrated skills, and relevant qualifications regardless of background.