About the Role
The role involves developing and iterating on machine learning models with a focus on reinforcement learning, contributing to both research and engineering efforts to accelerate training pipelines and improve system capabilities.
Responsibilities
- Design and implement reinforcement learning experiments
- Optimize training infrastructure for scalability and speed
- Collaborate on improving model training efficiency
- Iterate on algorithms to enhance learning stability
- Develop tools for monitoring and analyzing training runs
- Support large-scale model training workflows
- Refine reward modeling techniques
- Debug and resolve issues in training pipelines
- Contribute to versioning and reproducibility of experiments
- Work closely with researchers to prototype new ideas
- Improve data processing pipelines for training
- Evaluate model behavior during training phases
- Integrate feedback mechanisms into learning loops
- Assist in ablation studies to validate design choices
- Optimize resource utilization across compute clusters
- Document methods and results for internal review
- Ensure consistency across experimental setups
- Support deployment of training systems in production-like environments
- Develop automated testing for training components
- Contribute to cross-team knowledge sharing
- Analyze training dynamics to inform future iterations
- Implement safety checks within learning frameworks
- Refactor code for maintainability and performance
- Assist in benchmarking against prior approaches
- Help define success metrics for training objectives
Nice to Have
- PhD in computer science or related field
- Prior research in reinforcement learning
- Contributions to open-source machine learning projects
- Experience with large language models
- Work on safety or alignment in AI systems
- Publications in machine learning venues
- Familiarity with formal verification methods
- Experience with policy gradient methods
- Knowledge of human-in-the-loop training
- Background in software engineering best practices
- Experience mentoring junior engineers
- Work with real-time feedback systems
- Understanding of ethical AI development
- Prior role in a research-forward organization
- Involvement in model interpretability efforts
Compensation
Competitive salary based on experience and location
Work Arrangement
Hybrid work model with office and remote options
Team
Part of a research-focused team advancing core machine learning capabilities
Research Focus
- Work will center on accelerating reinforcement learning pipelines
- Emphasis on improving training speed and model reliability
- Projects aim to reduce iteration time for new ideas
Impact
- Engineers directly influence model safety and performance
- Work contributes to foundational improvements in training systems
Available for qualified candidates
