Summary:

The HEART AI Application Engineering team is seeking an AI Software Engineer to join our team. The ideal candidate will have industry experience working on AI infrastructure related topics. The position will involve taking these skills and applying them to solve some of the most crucial and exciting problems that exists on the web.

Required Skills:

Senior Software Engineer, Systems ML - AI Application Engineering Responsibilities:

1. Apply in depth knowledge of AI infrastructure and hardware acceleration techniques to build and optimize our intelligent machine learning systems that improve Meta’s product and experiences

2. Set direction and goals for the team related to project impact, AI system design, infrastructure, and developer efficiency

3. Directly or influencing partners to deliver impact through deep, thorough data-driven analysis

4. Define use cases and develop methodology and benchmarks to evaluate different strategies

5. Lead large and complex technical efforts across many engineers and teams

6. Mentor other engineers, research scientists and improve the quality of engineering work in the broader team

7. Helps onboard new team members, provides mentorship, and enables successful ramp up on the team's code base

Minimum Qualifications:

Minimum Qualifications:

8. Bachelor’s degree in computer science or a related STEM field

9. Specialized experience in one or more of the following machine learning/deep learning domains: hardware accelerator architectures, machine learning compilers or ML systems, AI infrastructure, high-performance computing, performance optimizations or ML frameworks such as PyTorch

10. Proven C/C++ and Python programming skills in developing AI Systems infrastructure or AI algorithms

11. Experience with debugging in C++, Python and/or PyTorch

12. Defining and leading long-term plans for the team

13. Track record of mentoring and growing other engineers

Preferred Qualifications:

Preferred Qualifications:

14. Master's degree/PhD in computer science or related STEM field and strong experience in AI framework development or accelerating deep learning models on hardware architectures

15. Knowledge of AI hardware accelerator architectures and CPUs

16. Experience with recommender and ranking models

17. Experience with distributed systems and communication protocols such as MPI

18. Experience with CUDA/OpenMP/OpenCL programming targeting AI hardware accelerators. Experience in accelerating libraries on AI hardware, like cuBLAS, cuDNN, CUTLASS, HIP, ROCm etc.

19. Technical leadership experience

Industry: Internet