Machine Learning Fellow - Human Frontier Collective (UK)

<p><strong>PLEASE NOTE:</strong> 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.</p> <h3><strong>About the Program</strong></h3> <p>The<strong> Human Frontier Collective (HFC) Fellowship</strong> 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.</p> <h3><strong>What You'll Do</strong></h3> <ul> <li><strong>ML Projects: </strong>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.</li> <li><strong>HFC Community:</strong> Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.</li> <li><strong>Contribute to Research Publications:</strong> Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., <a href="https://scale.com/research/scipredict"><em>SciPredict</em></a>, <a href="https://scale.com/research/propensitybench"><em>PropensityBench</em></a>, <a href="https://scale.com/research/prbench"><em>Professional Reasoning Benchmark</em></a>).</li> </ul> <h3><strong>Who Should Apply</strong></h3> <ul> <li><strong>Education: </strong>PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.</li> <li><strong>Professional Background: </strong>1-3+ years of experience as a Machine Learning Engineer or Data Scientist.</li> <li><strong>Skills: </strong>Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.</li> <li><strong>Professional Mindset:</strong> Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.</li> </ul> <h3><strong>Why Join the HFC?</strong></h3> <ul> <li><strong>Professional Development: </strong>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.</li> <li><strong>Join a Top-Tier Network: </strong>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.</li> <li><strong>Flexible Schedule: </strong>Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.</li> <li><strong>Competitive Pay:</strong> $80-100/hour, depending on projects, skills assessment, and location.</li> </ul> <h3><strong>Application Process</strong></h3> <ol> <li><strong>Apply: </strong>We review applications on a rolling basis.</li> <li><strong>Interview:</strong> Candidates will get to discuss their research experience, professional background, and alignment with our mission to advance human-centered AI.</li> <li><strong>Join the Collective:</strong> Successful candidates will receive an invitation to join the Human Frontier Collective Fellowship.</li> </ol><div class="content-conclusion"><p><strong><em>PLEASE NOTE: </em></strong><em>Our policy requires a 90-day waiting period before re ... (truncated, view full listing at source)