SWE Bench – Data Engineer/Data Scientist

About this opportunity

Turing is seeking experienced Software Engineers with Data Engineering or Data Science expertise to contribute to benchmark-driven evaluation projects focused on real-world data workflows. The role involves working with production-like structured and unstructured datasets, designing and validating data pipelines, performing data processing and analysis, preparing features for data science use cases, modifying Python code locally, evaluating data quality and reproducibility, and collaborating with researchers and engineers to create challenging tasks for advanced AI systems.

What the listing describes

• ✓Work with structured and unstructured datasets to support SWE Bench-style evaluation tasks
• ✓Design, build, and validate data pipelines used in benchmarking and evaluation workflows
• ✓Perform data processing, analysis, feature preparation, and validation for data science use cases
• ✓Write, run, and modify Python code to process data and support local experiments
• ✓Evaluate data quality, data transformations, and generated outputs for correctness and reproducibility
• ✓Create clean, well-documented, and reusable data workflows suitable for benchmarking
• ✓Navigate and modify real-world software codebases related to data engineering and data science
• ✓Participate in code reviews to maintain high standards of code quality and maintainability
• ✓Collaborate with researchers and engineers to design challenging real-world data engineering and data science tasks for AI systems
• ✓Validate benchmark workflows and ensure that technical outputs are reproducible
• ✓Analyze complex data-intensive problems and develop appropriate computational solutions

Requirements shown on the source

• ✓Minimum 3 years of overall experience as a Data Engineer, Data Scientist, or data-focused Software Engineer
• ✓Strong proficiency in Python for data engineering and data science workflows
• ✓Demonstrable experience with data processing and analysis
• ✓Experience with model-related data science workflows
• ✓Solid understanding of machine learning and data science fundamentals
• ✓Experience working with structured and unstructured data
• ✓Ability to understand, navigate, and modify complex real-world codebases
• ✓Experience writing readable, reusable, maintainable, and well-documented code
• ✓Strong problem-solving skills, including experience with algorithmic or data-intensive problems
• ✓Ability to work with production-like datasets and realistic data workflows
• ✓Ability to evaluate data quality, transformations, and outputs for correctness and reproducibility
• ✓Excellent spoken and written English communication skills
• ✓Ability to work independently in a remote environment
• ✓Availability for at least 4 hours per day and a minimum of 20 hours per week
• ✓Ability to provide 4 hours of overlap with PST working hours

Relevant backgrounds mentioned

• ✓Experience with SWE Bench or similar software engineering benchmarks
• ✓Experience creating benchmark-driven AI evaluation tasks
• ✓Experience working with production-grade data pipelines
• ✓Experience with machine learning workflows
• ✓Experience collaborating with AI researchers
• ✓Experience working with both structured and unstructured datasets
• ✓Experience modifying and debugging complex real-world codebases
• ✓Experience with automated data validation and reproducibility workflows
• ✓Experience participating in software engineering code reviews
• ✓Experience with Python-based data science and machine learning ecosystems