About the Role
This position involves building and maintaining software systems that enable the development and deployment of reinforcement learning models, bridging research and production environments through robust full-stack engineering practices.
Responsibilities
- Design and implement full-stack systems that support reinforcement learning workflows
- Collaborate with research teams to translate experimental prototypes into production-grade code
- Build scalable backend services to manage training pipelines and model evaluation
- Develop user interfaces for monitoring and interacting with AI training processes
- Optimize system performance for large-scale distributed training runs
- Ensure robustness, reliability, and observability across all system components
- Integrate machine learning models into end-to-end software platforms
- Work closely with infrastructure teams to align with security and compliance standards
- Maintain clean, well-documented codebases with comprehensive testing
- Troubleshoot and resolve issues across the stack in production environments
- Contribute to architectural decisions for new features and systems
- Support deployment automation and continuous integration pipelines
- Help define best practices for software engineering within the AI research context
- Iterate quickly based on feedback from researchers and product stakeholders
- Balance rapid prototyping with long-term maintainability and scalability
Nice to Have
- Experience contributing to machine learning training pipelines
- Background in reinforcement learning or related AI subfields
- Prior work on systems supporting human-in-the-loop training
- Familiarity with frontend frameworks such as React or Angular
- Knowledge of authentication, authorization, and security best practices
- Experience with large-scale data processing systems
- Contributions to open-source projects in AI or systems engineering
- MS or PhD in Computer Science or related discipline
Compensation
Competitive salary and benefits package commensurate with experience
Work Arrangement
Hybrid or remote options available; some roles may require office presence depending on team needs
Team
A rapidly expanding team of dedicated researchers, engineers, policy specialists, and business leaders focused on advancing AI safety and capabilities
Research Collaboration
Engineers regularly partner with research teams to implement novel algorithms and evaluate system performance in real-world settings
Focus on Safety
All systems are designed with safety, interpretability, and alignment considerations integrated throughout the development lifecycle
Engineering Culture
Emphasis on code quality, peer review, documentation, and iterative improvement
Impact
Work directly contributes to advancing the state of AI systems while maintaining rigorous safety standards
Growth Opportunities
Opportunities to lead projects, mentor junior engineers, and shape technical direction as the team scales
Visa sponsorship available for qualified candidates
