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Turing
Remote Ml Engineer Mumbai (India)
Turing
Mumbai•On-site•1-3 Yrs•Jun 26
Job description
Overview
About Turing: 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.
Responsibilities
- 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 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 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.
- 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 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 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.
Qualifications
- Minimum 3+ years of overall About Turing:
- Minimum 3+ years of overall