About the Role
The fellows will engage in hands-on research and development in reinforcement learning, collaborating with experienced scientists to explore novel approaches and improve system performance.
Compensation
Competitive salary and benefits package provided for the duration of the program
Work Arrangement
Full-time, on-site or hybrid availability may be required depending on team needs
Team
Work within a research-focused team advancing methods in machine learning and artificial intelligence
Program Overview
- The Fellows Program is designed for individuals beginning their careers in machine learning research.
- Participants spend time immersed in technical projects aligned with long-term AI safety and capability goals.
- The program emphasizes mentorship, skill development, and integration into active research workflows.
Responsibilities
- Design and implement reinforcement learning algorithms.
- Run experiments and analyze performance metrics.
- Collaborate with researchers to refine training processes.
- Document findings and contribute to internal reports.
- Iterate on models based on empirical feedback.
- Support infrastructure improvements for training pipelines.
- Engage in peer discussions and technical reviews.
Qualifications
- Bachelor’s or master’s degree in computer science, engineering, or related technical field.
- Strong foundation in machine learning concepts and statistical methods.
- Experience implementing deep learning models using frameworks such as PyTorch or JAX.
- Familiarity with reinforcement learning techniques and environments.
- Proficiency in Python and experience with numerical computing libraries.
- Ability to work independently and solve complex technical problems.
- Prior research experience, either academically or professionally, is highly valued.
Preferred Qualifications
- Experience with large-scale model training.
- Background in AI safety, interpretability, or alignment research.
- Contributions to open-source machine learning projects.
- Publications or preprints in relevant fields.
- Knowledge of distributed computing systems.
Duration and Commitment
- The program typically lasts between six months and one year.
- Fellows are expected to be fully dedicated during the appointment period.
- Extensions may be considered based on performance and project needs.
Application Process
- Applicants must submit a resume, cover letter, and academic transcript.
- Shortlisted candidates will undergo technical interviews.
- Final decisions are based on technical ability, research potential, and team fit.
Learning Outcomes
- Gain practical experience in advanced reinforcement learning systems.
- Develop research fluency in AI safety and model behavior.
- Improve engineering skills through real-world implementation challenges.
- Receive mentorship from experienced research scientists.
Visa sponsorship may be available for qualified international applicants
