Community ML Research Engineer, non-AI scientific fields - US Remote

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 300k apps. Our open-source libraries have more than 400k+ stars on Github. We focus on developing open-source tools and models that push the boundaries of AI while remaining efficient and user-friendly.

As a Scientific Machine Learning Research Engineer, you will bridge the gap between cutting‑edge machine learning and scientific research in fields like biology, physics, or quantum research. You’ll be a technical generalist
—equally comfortable diving into complex data pipelines, optimizing fast‑reads for distributed scientific datasets, and collaborating with researchers to build impactful ML tools. You’ll be responsible for:

• Building and optimizing datasets and data pipelines
  for scientific use cases, with a focus on fast, scalable reads across distributed file systems (e.g., HPC, cloud, or hybrid environments).
• Developing and adapting ML tools
  (not just models) to address real‑world scientific challenges, from data preprocessing to model deployment.
• Collaborating with non‑AI scientific communities
  to co‑design solutions, publish datasets, and create open‑source resources that lower the barrier to ML adoption in traditional sciences.
• Engaging with researchers and institutions
  to identify high‑impact opportunities, whether through hands‑on technical work or strategic partnerships.

This role is for technical problem‑solvers
who thrive in ambiguity. You might prototype a dataset pipeline one week, debug a distributed file system bottleneck the next, or co‑author a tutorial to onboard a new research community. We value curiosity, adaptability, and a willingness to roll up your sleeves—whether the challenge is technical or collaborative.

You’re a jack‑of‑all‑trades Research Engineer
with a passion for making ML accessible to scientific domains. You’ve likely:

• Built or optimized datasets, data pipelines, or tools
  for scientific applications, especially in distributed or high‑performance computing environments.
• Worked with fast‑reads, distributed storage, or large‑scale data processing
  —bonus if you’ve tackled challenges like cross‑file system data access or real‑time scientific data workflows.
• Collaborated with non‑AI research communities
  (e.g., biology, physics, chemistry) to translate their needs into technical solutions, whether through code, documentation, or open‑source contributions.
• Experimented with diverse ML approaches
  (not just large models) to solve domain‑specific problems, and enjoy iterating based on feedback from end‑users.

You’ll enjoy working here if you:

• Are a technical generalist
  who loves both the “weeds” (e.g., optimizing a dataset pipeline) and the “big picture” (e.g., shaping a collaboration’s long‑term impact).
• Thrive in fast‑paced, ambiguous environments
  and can pivot between technical deep dives and cross‑team communication in Hugging Face’s decentralized culture.
• Believe the best solutions often come from iterative experimentation
  —whether it’s testing a new data format, prototyping a tool, or refining a community workshop.

If you’re interested in joining us, but don’t tick every box above, we still encourage you to apply! We’re building a diverse team whose skills, experiences, and backgrounds complement one another. We’re happy to consider where you might be able to make the biggest impact.

Checkout  for more information about the science team at Hugging Face.

We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

You will work with some of the smartest people…