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About the role
We are looking for a Software Engineer to build the common infrastructure for data curation at MEDC. Today, data curation work across MEDC and our modeling partners (e.g., semantic and content QA datasets, generative retrieval evals) happens largely in ad-hoc notebooks, with no shared capabilities or standardization. This makes every new curation project slow to launch, hard to discover, and labor-intensive to productionize. You will turn that into a coherent, reusable platform. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong software engineering in Python, with experience building reusable infrastructure, libraries, or frameworks used by other engineers and researchers
- Experience building LLM-driven data generation or transformation pipelines (e.g., synthetic data, structured outputs, batch inference at scale)
- Hands-on experience with data quality methods: sampling strategies, filtering, deduplication, and model-based quality scoring such as LLM-as-judge
- Modeling intuition: an understanding of how data choices affect model behavior, and the ability to design experiments that measure it
- Experience with distributed data processing (e.g., Spark, Ray, or similar)
- Excellent collaboration skills, particularly with researchers, data scientists, and platform teams
More about this role
At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
Model Evaluations and Data Curation ("MEDC") forms the flywheel of foundation model development at Netflix. We build the benchmarks, evaluators, and baselines that guide progress on our foundation models, and the data infrastructure that delivers high-quality, reproducible training and evaluation datasets to our AI/ML researchers.
Together, these capabilities create a continuous loop of data → train → evaluate → adapt, driving faster and more confident innovation. Our work is upstream of nearly every AI-powered member experience at Netflix, making our impact unusually broad for a team of this size.
- Data Curation: Selecting, cleaning, and organizing raw data to create the best possible training sets for our models. The more abstractions we can make on the data, the faster we can innovate.
- LLM Evaluations:...
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