Member of Technical Staff - Frontier System Modelling
About The Position
SemiAnalysis is seeking a highly motivated Member of Technical Staff to join their engineering team, focusing on system modeling for AI clusters exceeding 100,000 chips. This role offers a unique opportunity to contribute to high-visibility open-source projects, architecting and modeling performance for next-generation AI chips. The ideal candidate is passionate about performance engineering, system modeling, first principles, and working at the intersection of hardware and software, aiming to make an industry-wide impact. As part of the interview process, candidates will complete a paid coding challenge that reflects typical daily tasks at SemiAnalysis. Compensation is competitive and will depend on experience, skillset, location, and business needs.
Requirements
- Strong skills in Python.
- Deep understanding about disaggregated prefill, wide expert parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, etc.
- Experience with frontier MoE training and inference workloads.
- Knowledgeable about GEMM sizes & proportions of wall time & FLOPs on every major operator in an modern transformer.
- Intuition about Amdahl principles, arithmetic intensity, weak & strong scaling.
- Previous experience as an ML engineer or kernel programmer.
Nice To Haves
- Develop deep expertise in large-scale system modelling for AI infrastructure, including performance prediction across hyperscale (100k+ chip) clusters.
- Gain a strong first-principles understanding of how hardware, software, and model architectures interact to determine real-world performance.
- Build advanced knowledge of parallelism strategies and scaling techniques across frontier LLM training and inference workloads.
- Strengthen the ability to translate low-level system behavior into high-level performance insights, including cost efficiency and power optimization.
- Gain hands-on exposure to cutting-edge AI hardware across multiple vendors and architectures, staying at the forefront of industry developments.
- Contribute to high-impact open source projects and build visibility within the AI and infrastructure engineering community.
- Take ownership of complex modelling frameworks and drive independent research directions as you grow within the team.
- Develop the ability to influence technical strategy and decision-making through data-driven insights and performance analysis.
Responsibilities
- Develop a complex model in Python to predict performance (MFU, tok/s/gpu, tok/s/user) on current & future AI chip projects across both frontier LLM training & inference models.
- Implement modern parallelism & inference strategies to generate Pareto frontier roofline curves for performance on each chip.
- Develop microbenchmarks across multiple vendors (AMD, NVIDIA, TPU, Trainium) to calibrate the system model to as close to reality as possible.
- Build BoM estimates to model performance per total cost of ownership and performance per watt.
Benefits
- Competitive Compensation depending on experience, skillset, location & business needs
- Paid coding challenge
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
