Member of Technical Staff - Inference

Employment Type:

Full-Time
Work Setting:
In-office/Remote

Work Location:

United States, New York, Mexico, San Francisco, Canada

Work Hours:

Office hours
Find out more here:

Semi Analysis is an independent research and analysis firm specializing in the Semiconductor and AI industries. Our in-depth coverage spans the entire supply chain, from semiconductor fabrication processes to cutting-edge AI Models, software, and infrastructure. We are recognized as the leading authority on the semiconductor supply chain, with the highest concentration of industry experts within one team, and a deep-rooted passion for delving into the intricacies.

We’re a global team of over 50 analysts, each with extensive networks across the semiconductor supply chain and AI ecosystem, publishing industry shaping articles while participating in 40+ conferences annually.

Our newsletter reaches more than 200,000 subscribers worldwide, including senior management and c-suite leaders at the leading semiconductor and AI companies.

We also offer three core products:

• Industry Models – we develop and publish industry models on accelerator shipments, data centre demand and supply, GPU total cost of ownership, and more. We work with hyperscalers, neoclouds, many of the world’s largest hedge funds, and government agencies.
• Core Research – our public equity markets product, geared towards financial investors, distils our deep technical research and knowledge into key insights on technology and product trends.
• Consulting and Technical Due Diligence – We conduct custom research and project work to guide key strategic and investment decisions for the largest private equity funds, leading venture capital firms, companies across the AI ecosystem, and government agencies.

• Member of Technical Staff will play a crucial role in developing training & inference benchmarks & system modelling.
• You will also play a role in writing and preparing our newsletter articles for publishing, with the opportunity for direct authorship recognition.
• We are hiring individual contributors at all experience levels.
• As part of the interview process, you’ll complete a paid coding challenge designed to reflect typical daily tasks at Semi Analysis.

• Conduct training & inference performance benchmarks across various AI hardware (e.g. NVIDIA H100, AMD Mi300X, Google TPUs, AWS Trainium2) using frameworks such as PyTorch, JAX, vLLM, SGLang, etc.
• Author detailed technical research reports analyzing benchmark results, hardware performance, scalability, & efficiency.
• Develop comprehensive system modelling using Python & NCCL for existing & future AI compute clusters, scaling from single-GPU setups to O(100k) GPU clusters.
• Establish and maintain strategic partnerships & collaborations with over 50 leading neocloud providers & AI chip manufacturers, including AMD, NVIDIA, and other industry stakeholders.
• Stay current on emerging trends & technologies by attending major industry & academic conferences such as NeurIPS, MLSys, NVIDIA GTC, AMD’s Advancing AI, etc.

• Proactive, self-motivated, and capable of working independently in a global team.
• Demonstrated experience in ML frameworks such as PyTorch or JAX through professional experience, personal projects, or personal Substack blogs.
• Solid understanding of at least 1 of the following: transformer architecture, LLM parallelism strategies, and/or CUDA parallel programming.
• Strong research skills and the ability to synthesize information from various sources to draw insights.
• Undergraduate degree in Computer Science, Engineering or other relevant technical field is not required.
• If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you are passionate about tech & have a great work ethic, Semi Analysis is the place for you.

• Develop deep expertise in AI infrastructure, including training and inference optimization across leading hardware platforms and large-scale compute environments
• Gain hands‑on experience modelling and scaling distributed systems, from single‑node setups to hyperscale GPU clusters
• Build strong technical writing…