- Apply state-of-the-art AI infrastructure and software/hardware acceleration techniques to build and optimize our large-scale AI workloads
- Analyze, benchmark, and optimize large-scale workloads on next-generation training superclusters
- Define use cases and develop methodology and benchmarks to evaluate different approaches
- Set direction and goals for the team related to project impact, AI system design, infrastructure, and developer efficiency
- Lead large and complex technical efforts across many engineers and teams
- Influence and impact next-generation of model and hardware architecture choices based on thorough data-driven analyses
- Helps onboard new team members, provides mentorship, and enables successful ramp up on the team's code base
- Mentor other engineers, research scientists and improve the quality of engineering work in the broader team
- Bachelor’s degree in computer science or a related STEM field
- Specialized experience in one or more of the following machine learning/AI domains: hardware accelerator architectures, machine learning compilers or ML systems, AI infrastructure, high-performance computing, performance optimizations or ML frameworks such as PyTorch
- Proven C/C++ and Python programming skills in developing AI Systems infrastructure or AI algorithms
- Experience with debugging in C++, Python and/or PyTorch
- Track record of defining and leading long-term plans for the team
- Track record of mentoring and growing other engineers
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
- Technical leadership experience
- Master's degree/PhD in computer science or related STEM field and experience in AI framework development or accelerating AI models on hardware architectures
- Experience with training of large-scale AI models
- Experience with distributed AI systems and communication protocols such as MPI or collective libraries such as NCCL
- Experience or knowledge in one or more of LLM/LDM, ranking and recommender models or collective communication libraries
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