SWE Bench – Data Engineer/Data Scientist
Overview
Turing, a San Francisco-based research accelerator, is hiring experienced data engineers and data scientists for benchmark-driven evaluation of advanced AI systems. The role centers on SWE Bench-style tasks: building and validating data pipelines, processing structured and unstructured datasets, and preparing features for data science workflows. You will write Python code, run local experiments, and verify outputs for correctness and reproducibility. This is a fully remote contractor assignment with required overlap with PST hours.
What You'll Do8
- 1Process structured and unstructured datasets to support SWE Bench-style evaluation tasks.
- 2Design, build, and validate data pipelines for benchmarking and evaluation workflows.
- 3Perform data cleaning, analysis, feature preparation, and validation for data science use cases.
- 4Write and modify Python code to process data and run local experiments.
- 5Assess data quality, transformations, and outputs for correctness and reproducibility.
- 6Create clean, documented, reusable data workflows for benchmarking.
- 7Review peers' code to maintain code quality and maintainability standards.
- 8Collaborate with researchers and engineers to design real-world data engineering and data science tasks for AI systems.
Requirements8
- 1At least 3 years of experience as a Data Engineer, Data Scientist, or data-focused Software Engineer.
- 2Strong command of Python for data engineering and data science workflows.
- 3Proven experience with data processing, analysis, and model-related workflows.
- 4Solid grasp of machine learning and data science fundamentals.
- 5Hands-on experience with both structured and unstructured data.
- 6Ability to understand, navigate, and modify complex, real-world codebases.
- 7Track record of writing readable, reusable, maintainable, and well-documented code.
- 8Strong problem-solving skills on algorithmic or data-intensive problems, plus clear spoken and written English.
Who Should Apply
The ideal candidate has 3+ years of hands-on data engineering or data science experience and can write production-quality Python under time pressure. They should enjoy untangling messy data and making pipelines reproducible for benchmark tasks. Someone who prefers dashboarding or high-level analytics over hands-on pipeline work will be a weaker fit. A common reason candidates fail is stumbling on the 60-minute live coding challenge, especially when asked to process data quickly. Others get screened out when they cannot explain how they validate data quality or reproduce results in past projects.
Location
Required Skills
Application Tip
Practice a 60-minute timed coding exercise that involves building a small data processing pipeline in Python. Be ready to talk about how you validated data quality and ensured reproducibility in a recent project, since that is a core theme of the evaluation.
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How your application is processed
1Application received
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2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
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