About the Role
The role involves building and refining machine learning systems with a focus on reinforcement learning, contributing to core research initiatives, and accelerating model development cycles.
Responsibilities
- Design and implement reinforcement learning algorithms
- Optimize training pipelines for efficiency and scalability
- Collaborate with researchers to prototype new model architectures
- Evaluate model performance using rigorous testing frameworks
- Debug and improve system-level issues in training infrastructure
- Contribute to codebases supporting large-scale experiments
- Analyze training dynamics to inform research direction
- Support deployment of experimental models into test environments
- Refactor and maintain core machine learning software modules
- Instrument systems to collect detailed training metrics
- Improve data throughput in distributed training setups
- Integrate feedback from safety and alignment teams
- Ensure code meets performance and reliability standards
- Document technical designs and implementation details
- Assist in benchmarking against prior state-of-the-art methods
- Work closely with infrastructure engineers on system improvements
- Help define best practices for experimental tracking
- Contribute to internal tools for model analysis
- Support reproducibility of research results
- Participate in technical discussions on algorithm design
- Monitor training runs for anomalies or regressions
- Optimize resource utilization across compute clusters
- Implement safeguards for stable training behavior
- Translate research concepts into functional code
- Iterate rapidly based on empirical results
Nice to Have
- Advanced degree in a relevant field
- Prior work in reinforcement learning research
- Contributions to open-source machine learning projects
- Experience with transformer-based architectures
- Knowledge of safety evaluation methods
- Publication record in machine learning venues
- Hands-on experience with large model training
- Familiarity with formal methods in AI safety
- Background in systems engineering for ML
- Experience mentoring junior engineers
- Understanding of ethical implications in AI development
- Involvement in interdisciplinary research teams
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid or remote options available
Team
Part of a research-focused machine learning team advancing reinforcement learning systems
Research Focus
- Work will center on improving reinforcement learning systems with an emphasis on stability, scalability, and alignment with intended behavior.
- Engineers will engage in both theoretical exploration and practical implementation to advance core capabilities.
Impact
- Contributions will directly influence the development of safer and more controllable AI systems.
- Work will support long-term research goals in reliable machine learning.
Available for qualified candidates
