MLE Bench – ML Engineers
Overview
This contract role centers on benchmark-driven evaluation of real-world machine learning systems. You will work directly with production-grade codebases to build, run, and modify model training, evaluation, and inference pipelines. The work blends research and engineering, using frameworks such as PyTorch, TensorFlow, or JAX and collaborating with researchers to design challenging evaluation tasks. The position is fully remote and requires a minimum 20-hour week with a 4-hour daily overlap with PST.
What You'll Do8
- 1Support MLE Bench-style evaluation tasks by working directly with real-world ML codebases.
- 2Develop, execute, and adjust model training, evaluation, and inference pipelines.
- 3Assemble datasets, engineer features, and define metrics for benchmarking and validation.
- 4Identify and fix bugs, refactor code, and enhance production-like ML systems for correctness and speed.
- 5Analyze model behavior, failure modes, and edge cases in benchmark scenarios.
- 6Produce clean, reproducible, and well-documented Python code for ML workflows.
- 7Take part in code reviews to ensure high engineering standards.
- 8Work alongside researchers and engineers to craft challenging ML engineering tasks for evaluating AI systems.
Requirements9
- 1At least 3 years of experience as a Machine Learning Engineer or ML-focused Software Engineer.
- 2Advanced Python skills for machine learning and data workflows.
- 3Hands-on experience building and tuning model training, evaluation, and inference pipelines.
- 4Solid grasp of ML fundamentals, including supervised and unsupervised learning, evaluation metrics, and optimization.
- 5Experience with ML frameworks like PyTorch, TensorFlow, or JAX.
- 6Ability to read, navigate, and modify complex real-world ML codebases.
- 7Track record of writing readable, reusable, and maintainable production-quality code.
- 8Strong problem-solving and debugging abilities.
- 9Excellent spoken and written English communication skills.
Who Should Apply
The candidate who thrives here brings at least 3 years of ML engineering experience and can move fluidly between research ideas and production code. This role is less suitable for engineers who prefer model experimentation only and avoid dealing with messy, real-world codebases. A common reason candidates get rejected is struggling with the 60-minute live coding challenge, especially when asked to modify an existing ML pipeline under time pressure. Others score low on fit when they cannot articulate evaluation metric choices or fail to demonstrate strong debugging skills.
Location
Required Skills
Application Tip
Practice refactoring an existing ML training pipeline in Python within a 60-minute window. Be ready to explain your evaluation metric choices and debug code out loud, as the live coding interview will test both speed and ML fundamentals.
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How your application is processed
1Application received
Your resume and details are logged the moment you apply.
2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
3Employer sees qualified profiles only
Only candidates who clear screening move forward.
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