Machine Learning Fellow

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Become a member of the Human Frontier Collective, a diverse network of experts across 70+ disciplines.

• Engage in selected AI/ML initiatives with Scale AI and associated labs and platforms.

• Establish AI safety and policy frameworks.

• Design, evaluate, and enhance PyTorch models.

• Assess intricate ML code and AI-generated solutions for both efficiency and accuracy.

• Analyze AI models and create experimental designs.

• Develop reinforcement learning environments.

• Partner with Scale’s research team to co-write technical reports and research articles.

• Receive regular assignments for carefully chosen partner projects.

⛳️ Requirements

• PhD or postdoctoral research experience in Machine Learning, Computer Science, or a related domain; or extensive industry experience in Machine Learning or a related area.

• Strong command of Python and contemporary ML frameworks, including PyTorch and TensorFlow.

• Familiarity with cloud infrastructures such as AWS is advantageous.

• Experience with MLOps tools like Docker is beneficial.

• Knowledge of LLM frameworks such as LangChain is a plus.

• Detail-oriented and innovative thinker with a passion for applied AI research and a commitment to teamwork.

• Must possess or independently secure full-time work authorization in the country of residence.

• Scale AI does not provide visa sponsorship for Fellows.

🏝️ Benefits

• Fully remote position with no predetermined end date; engagements persist as long as there is mutual interest.

• Flexible working hours with no minimum commitment; fellows have the autonomy to choose their projects.

• Access to a community network, including newsletters, discussion platforms, virtual sessions, and in-person events.

• Opportunities to co-author select research publications.

• Access to an exclusive interdisciplinary expert network.

• Competitive pay rates for projects, varying based on project specifics, scope, skillset, and location.

• Most fellows allocate 10–25 hours per week according to their self-determined schedules.