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Turing, a leading AI research accelerator, seeks experienced ML Engineers for freelance benchmarking work. You will handle production-grade ML codebases, train and evaluate models, and build robust pipelines for benchmarking and validation. Collaboration with researchers and engineers is essential.
Applicants should have 3+ years of ML experience, strong Python, and familiarity with PyTorch, TensorFlow, or JAX. Remote work is offered, with flexibility and international clients.
TuringBased 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
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.
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)
Location: India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Brazil, Mexico
Technical Interview with live coding challege (60 mins)