Computer Science & Machine Learning Fellow
Computer Science & Machine Learning Fellow is an open role at Human Frontier Collective (HFC), available remotely.
Applications open on Human Frontier Collective (HFC)’s own site. How we make money.
About this role
Please note: This is a fully remote contract opportunity, with an estimated duration of 6 months with the opportunity to be extended. To be eligible, you must have authorization to work in the country you 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 Collaborative Work Get invited to engage in high-impact projects with our partnered AI labs and platforms. Design and review advanced deep learning problems while analyzing and critiquing complex ML code and AI-generated PyTorch implementations. Apply expert judgment on GPU performance, profiling, and hardware-aware architecture and scaling 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., ASPI , SciPredict , ResearchRubrics , Humanity's Last Exam ). Who Should Apply Education : Advanced degree (PhD or higher) in Computer Science, Electrical/Computer Engineering, Applied Math, AI/ML, or a related field. Deep Learning Experience : Hands-on experience building and fine-tuning deep learning models in PyTorch, including architectures like Transformers, CNNs, and diffusion models. GPU & Optimization : Familiarity with GPU performance and optimization, including memory management, CUDA kernels, and profiling tools. Code Review : Skilled at analyzing research-level model code
Pay
Not disclosed
Human Frontier Collective (HFC) doesn’t publish a rate for this listing - it isn’t missing from our data.
About Human Frontier Collective (HFC)
Human Frontier Collective (HFC)'s connection to Scale AI: Scale fellowship / expert network. Research, STEM, medicine, finance, legal and ML model evaluation
Ownership confidence: Confirmed. See the full network profile →
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Frequently asked questions
How do I apply for the Computer Science & Machine Learning Fellow role at Human Frontier Collective (HFC)?
Applications go through Human Frontier Collective (HFC)'s own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Human Frontier Collective (HFC)'s access model is "Selective / vetted" (Selective application). This listing was first seen here on July 20, 2026 and was still live at the last check.
Is the Computer Science & Machine Learning Fellow role at Human Frontier Collective (HFC) remote?
Yes - Human Frontier Collective (HFC) lists this role as remote.
What does the Computer Science & Machine Learning Fellow role at Human Frontier Collective (HFC) pay?
Human Frontier Collective (HFC) doesn't state a rate on this listing. HumanSourcer never fills that gap with an estimate, so the absence is the source's, not a hole in this page. Human Frontier Collective (HFC)'s profile shows what its rate-carrying listings do advertise.
What kind of work is Computer Science & Machine Learning Fellow?
Research / analysis. Research, STEM, medicine, finance, legal and ML model evaluation
Is Human Frontier Collective (HFC) a legitimate AI-training platform?
Scale fellowship / expert network - and that relationship is rated "Confirmed" here because it can be checked against hfc.scale.com rather than taken on Human Frontier Collective (HFC)'s word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.
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