Deep Learning Solutions Engineer

Role Overview: You are an experienced Machine Learning Engineer (MLE Bench) who will contribute to benchmark-driven evaluation projects focused on real-world machine learning systems. Your responsibilities will include working with production-grade ML codebases, model training and evaluation pipelines, and deployment-oriented workflows to assess and enhance 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. Key Responsibilities: - 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 Required: - 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. - Solid problem-solving and debugging skills. - Excellent spoken and written English communication skills. About Turing: Turing is based in San Francisco, California and is the worlds leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers by accelerating frontier research with high-quality data, advanced training pipelines, and top AI researchers specializing in coding, reasoning, STEM, multilinguality, multimodality, and agents. Additionally, Turing applies its 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. Perks of Freelancing With Turing: - Work in a fully remote environment. - Opportunity to work on cutting-edge AI projects with leading LLM companies. Role Overview: You are an experienced Machine Learning Engineer (MLE Bench) who will contribute to benchmark-driven evaluation projects focused on real-world machine learning systems. Your responsibilities will include working with production-grade ML codebases, model training and evaluation pipelines, and deployment-oriented workflows to assess and enhance 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. Key Responsibilities: - 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 Required: - 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. - Solid problem-solving and debugging skills. - Excellent spoken and written English communication skills. About Turing: Turing is based in San Francisco, California and is the worlds leading research accelerator for