Machine Learning Fellow- Human Frontier Collective (US)

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ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., SciPredict, PropensityBench, Professional Reasoning Benchmark).

Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
Professional Background: 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
Skills: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.
Professional Mindset: Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.

Professional Development: High-impact experts expand their influence through review projects, advisory roles, and research, while deepening their AI expertise, strengthening analytical and problem-solving skills, and engaging with pioneering AI applications in science and technology.
Join a Top-Tier Network: Collaborate with a global network of engineers and experts to advance responsible AI through impactful, flexible research and training. 80% of our members come from leading institutions.
Flexible Schedule: Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.
Competitive Pay: Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location.

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