About Turing:

Based in San Francisco, California, Turing is the world’s 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 hands-on work with production-grade ML codebases, model training and evaluation pipelines, and deployment-oriented 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 day-to-day life look like?

• 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, and well-documented Python code for ML workflows.
• Participate in code reviews to ensure high standards of engineering quality.
• Collaborate with researchers and engineers to design challenging, real-world 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.
• Hands-on 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, real-world ML codebases.
• Experience writing readable, reusable, and maintainable production-quality code.
• Strong problem-solving 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 cutting-edge AI projects with leading LLM companies.

Offer Details:

• 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)

After applying, you will receive an email with a login link. Please use that link to access the portal and complete your profile.

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• 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, and well-documented Python code for ML workflows.
• Participate in code reviews to ensure high standards of engineering quality.
• Collaborate with researchers and engineers to design challenging, real-world ML engineering tasks for AI system evaluation.

• Strong proficiency in Python for machine learning and data workflows.
• Hands-on 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, real-world ML codebases.
• Experience writing readable, reusable, and maintainable production-quality code.
• Strong problem-solving and debugging skills.
• Excellent spoken and written English communication skills.

• 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.
• Hands-on 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, real-world ML codebases.
• Experience writing readable, reusable, and maintainable production-quality code.
• Strong problem-solving and debugging skills.
• Excellent spoken and written English communication skills.