About the Role
Develop and maintain software systems that integrate with JAX to improve performance on accelerated hardware. Collaborate with research and engineering teams to implement scalable solutions for AI workloads.
Responsibilities
- Design and implement core components for machine learning frameworks
- Optimize software performance on GPU-accelerated systems
- Collaborate with research teams to bridge theoretical models and production code
- Improve compilation and execution pipelines for numerical computing
- Enhance debugging and profiling tools for distributed training
- Contribute to open-source projects related to AI infrastructure
- Develop abstractions that simplify hardware-aware programming
- Support integration of new hardware features into software stack
- Ensure compatibility across software versions and platforms
- Troubleshoot complex system-level issues in high-performance environments
- Write clean, maintainable, and well-documented code
- Participate in code reviews and technical design discussions
- Drive performance improvements through algorithmic and system-level changes
- Work closely with compiler and runtime teams
- Maintain alignment with long-term software architecture goals
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model
Team
Part of the accelerated computing and AI infrastructure team
About the Team
This team focuses on advancing AI software infrastructure to maximize performance on accelerated computing platforms. Engineers work at the intersection of hardware and software to enable next-generation AI applications.
Preferred Qualifications
- PhD in computer science or related field
- Experience with XLA or other linear algebra compilers
- Contributions to JAX or similar open-source machine learning frameworks
Available for qualified candidates
