Remote Machine Learning Engineer (Benchmark Evaluation)

About the role

We are seeking an experienced Machine Learning Engineer to join Turing’s benchmark‑driven evaluation team. The role focuses on real‑world ML systems, handling production‑grade codebases, model training, evaluation pipelines, and deployment‑oriented workflows to improve advanced AI capabilities.

Key responsibilities

• Work with real‑world ML codebases to support MLE Bench‑style 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 production‑like ML systems for correctness and performance.
• Evaluate model behavior, failure modes, and edge cases relevant to benchmark tasks.
• Write clean, reproducible, well‑documented Python code for ML workflows.
• Participate in code reviews to ensure high engineering quality.
• Collaborate with researchers and engineers to design challenging real‑world ML engineering tasks for AI system evaluation.

Required profile

• Minimum 3 + years of experience as a Machine Learning Engineer or Software Engineer with an ML focus.
• Strong proficiency in Python for machine‑learning and data workflows.
• Hands‑on experience with model training, evaluation, and inference pipelines.
• Solid understanding of supervised and unsupervised learning, evaluation metrics, and optimization.
• Experience with major ML frameworks such as PyTorch, TensorFlow, or JAX.
• Ability to navigate and modify complex, production‑grade ML codebases.
• Excellent problem‑solving, debugging, and English communication skills.

Required skills

• Python
• PyTorch
• TensorFlow
• JAX

What we offer

• Fully remote work environment.
• Opportunity to contribute to cutting‑edge AI projects with leading LLM companies.
• Flexible schedule with a minimum of 20 hours per week and 4 hours overlap with PST.
• Freelance contract (no medical or paid leave).