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

New York, New York, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Scientific ResearchIndustries

Requirements

Experience in cutting-edge machine learning research and/or collaboration with research communities (e.g., Jean-Zay, Alan Turing Institute, PRACE, Lawrence Berkeley National Laboratory, Institute for Quantum Computing)
Generalist Research Engineer with ability to experiment with different models and maximize impact in the science machine learning community
Comfortable working in a fast-paced and ambiguous environment, including communicating and collaborating in a decentralized organization
Enjoys deep understanding of technical domains and willingness to dive into the details

Responsibilities

Build and facilitate non-consortium-based research collaborations with researchers in non-AI scientific fields (e.g., biology, physics, chemistry, quantum, fluid dynamics) to explore innovative applications of ML tools
Co-build ML tools and models for scientific use cases, co-developing solutions and publishing pre-trained models or datasets tailored to these domains
Educate and engage with the scientific community through tutorials, workshops, and open-source contributions to bridge the gap between ML and traditional sciences
Foster strategic partnerships and community research initiatives with academic institutions and organizations to advance interdisciplinary innovation and adoption

Skills

Machine Learning

ML Research

Biology

Physics

Chemistry

Quantum

Fluid Dynamics

Open Source Contributions

Tutorials

Workshops

Datasets

Pre-trained Models

Hugging Face

Develops advanced NLP models for text tasks

About Hugging Face

Hugging Face develops machine learning models focused on understanding and generating human-like text. Their main products include advanced natural language processing (NLP) models like GPT-2 and XLNet, which can perform tasks such as text completion, translation, and summarization. Users can access these models through a web application and a repository, making it easy to integrate AI into various applications. Unlike many competitors, Hugging Face offers a freemium model, allowing users to access basic features for free while providing subscription plans for advanced functionalities. The company also tailors solutions for large organizations, including custom model training. Hugging Face aims to empower researchers, developers, and enterprises to utilize machine learning for text-related tasks.

New York City, New YorkHeadquarters

2016Year Founded

$384.9MTotal Funding

SERIES_DCompany Stage

Enterprise Software, AI & Machine LearningIndustries

201-500Employees

Benefits

Flexible Work Environment

Health Insurance

Unlimited PTO

Equity

Growth, Training, & Conferences

Generous Parental Leave

Risks

Salesforce's exclusive partnerships may limit Hugging Face's access to training datasets.

Microsoft's rStar-Math technique could outperform Hugging Face's NLP models in specific tasks.

DeepSeek-V3's release may intensify competition in the ultra-large model space.

Differentiation

Hugging Face offers a massive library of over one million AI models.

The company provides a thriving online community for open-source AI collaboration.

Hugging Face's freemium model allows easy access to advanced NLP tools.

Upsides

Collaboration with Microsoft could enhance small model performance in mathematical reasoning.

Partnership with DeepSeek may boost Hugging Face's ultra-large model capabilities.

Integration with Salesforce's ProVision could improve multimodal AI capabilities.

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