MLE Bench - ML Engineers

Based in San Francisco, California, Turing is the worlds leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Role Overview

We are looking for experienced Machine Learning Engineers (MLE Bench) to contribute to benchmark-driven evaluation projects focused on real-world machine learning systems. This role involves handson work with productiongrade ML codebases, model training and evaluation pipelines, and deploymentoriented workflows to help assess and improve the capabilities of advanced AI systems.

The ideal candidate is comfortable bridging research and engineering, working deeply with models, data, and infrastructure in realistic ML environments.

What does daytoday life look like

Work with realworld ML codebases to support MLE Benchstyle evaluation tasks.

Build, run, and modify model training, evaluation, and inference pipelines.

Prepare datasets, features, and metrics for ML benchmarking and validation.

Debug, refactor, and improve productionlike ML systems for correctness and performance.

Evaluate model behavior, failure modes, and edge cases relevant to benchmark tasks.

Write clean, reproducible, and welldocumented Python code for ML workflows.

Participate in code reviews to ensure high standards of engineering quality.

Collaborate with researchers and engineers to design challenging, realworld ML engineering tasks for AI system evaluation.

Requirements

Minimum 3+ years of overall experience as a Machine Learning Engineer or Software Engineer (ML-focused).

Strong proficiency in Python for machine learning and data workflows.

Handson experience with model training, evaluation, and inference pipelines.

Solid understanding of machine learning fundamentals (supervised/unsupervised learning, evaluation metrics, optimization).

Experience working with ML frameworks (e.g., "PyTorch, TensorFlow, JAX, or similar").

Ability to understand, navigate, and modify complex, realworld ML codebases.

Experience writing readable, reusable, and maintainable production-quality code.

Strong problemsolving and debugging skills.

Excellent spoken and written English communication skills.

Perks of Freelancing With Turing

Work in a fully remote environment.

Opportunity to work on cuttingedge AI projects with leading LLM companies.

Commitments Required: At least 4 hours per day and minimum 20 hours per week with overlap of 4 hours with PST.

Engagement Type: Contractor assignment (no medical/paid leave)

Duration of Contract: 3 months (adjustable based on engagement)

Evaluation Process

Technical Interview with live coding challege (60 mins) .