Machine Learning Fellow - Human Frontier Collective (UK)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Fellow - Human Frontier Collective (UK) United Kingdom PLEASE NOTE: This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension. To be eligible, candidates must be authorized to work in the country they reside in. About the Program The Human Frontier Collective (HFC) Fellowship brings together top researchers and domain experts to collaborate on high-impact work that are shaping the future of AI. As an HFC Fellow, you'll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems-while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers. What You'll Do - 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 ). Who Should Apply - Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field. - Professional Background: 1-3+ years of experience as a Machine

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