At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 2M models, 700k datasets & 600k apps. Our open-source libraries have more than 600k+ stars on Github.

About the Role

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 filesystems (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 filesystem 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.

About You

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-filesystem 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 hf.co/science for more information about the science team at Hugging Face.

More about 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.

We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias toward impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.

We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer flexible parental leave and paid time off.

We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. However, this job offer is quite special as it's best if you are in-person in our new Paris office. We provide relocation packages if necessary.

We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.

We support the community. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.