ML Research Engineer (Data Engineering)

We are hiring Research Engineers to sit at the boundary of research and systems engineering. Our multimodal data spans trillions of tokens of video alongside rich neural and behavioral recordings, making this one of the most demanding dataloading challenges in frontier AI. You will own the systems that turn this large, heterogeneous data into training-ready multimodal streams for foundation model training and evaluation at scale. This means designing and building a state-of-the-art end-to-end dataloading stack: data formatting, preprocessing, filtering, sharding, caching, and streaming. You will build runtime interfaces that deliver data to distributed training jobs across GPU clusters with high throughput, reliability, and full observability. You'll have substantial autonomy to shape foundational technical decisions on a small, high-impact team.

You'll thrive in this role if you:

Are excited about working in a fast-paced, production-focused research lab that often requires switching between many hats

Have significant software engineering experience and can move quickly without sacrificing rigor

Are able to balance research goals with practical engineering constraints

Enjoy pair programming and deeply collaborative work

Are eager to learn more about machine learning research in a novel scientific domain

Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research

Have ambitious goals for AI progress and are excited to create the best outcomes over the long term

We offer:

The chance to work on one of the most scientifically consequential AI projects being pursued today

A small, world-class team where your contributions directly shape the science and the company

Competitive compensation and benefits, along with visa sponsorship

Strong mentorship and career development

Familiarity with multi-modal transformer architectures

Experience with distributed training environments and deep understanding of sharding models and data

Experience with ML workflow orchestrators (e.g. Prefect, Dagster, Airflow).

Experience with containerization, and scaling container orchestration (e.g. via Docker, Kubernetes)

Experience with performance-critical compiled or systems languages (e.g. Rust, C++, CUDA)

Proficiency with MLOps platforms for experiment tracking and reproducibility (e.g. MLflow, W&B)

Background in scientific computing, computational neuroscience, life sciences, or ML-adjacent research environments

We encourage you to apply even if you do not believe you meet every single qualification. If you don't see a role that fits, we encourage you to submit a general application and tell us how you'd like to contribute to our mission.