AI Analyst - Reinforcement Learning (RL)

SemiAnalysis•San Francisco, CA

1d•Onsite

About The Position

SemiAnalysis 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 are expanding our team to deepen our coverage of the broader AI ecosystem and its impact on real-world industries. This is an opportunity to join a fast-growing, intellectually rigorous, and globally recognized team that influences strategic decisions at the highest levels. At SemiAnalysis, you’ll work directly with senior analysts and decision-makers across financial markets, hyperscalers, energy companies, and governments. Our culture is collaborative, flat, and fast-paced—offering the chance to shape high-impact research in a space undergoing unprecedented technological disruption. Learn more: https://semianalysis.com Position Overview We’re hiring an AI Forward Deployment Analyst to study how AI models are actually deployed in production and how those deployment choices shape real-world economics. This role sits at the intersection of model architecture, inference infrastructure, and applied workloads across hyperscalers, enterprises, and startups. You will focus on how AI systems are operationalized—latency constraints, throughput targets, cost ceilings, reliability requirements—and how these real deployment decisions influence the effective cost of AI tokens and the economic viability of AI-driven products. Your work will directly inform SemiAnalysis’s AI Tokenomics and AI Economics frameworks used by investors, corporate strategists, and policymakers. This is a full-time role based in the United States.

Requirements

Background in ML/AI, CS, or systems engineering (formal or self-taught).
Strong understanding of AI inference systems and production constraints.
Analytical mindset with interest in AI economics and real-world deployment.
Comfortable bridging technical depth with economic reasoning.
Full-time role based in the United States.

Responsibilities

Analyze real-world AI deployment patterns across hyperscalers, enterprises, and emerging AI-native startups.
Evaluate inference-time tradeoffs across hardware platforms, model architectures, batching strategies, and serving frameworks.
Model the economic impact of latency, throughput, availability, and scaling constraints on AI product margins.
Diligence AI companies deploying models in production, with a focus on cost structure, infrastructure choices, and unit economics.
Translate deployment-level technical decisions into investment-grade economic insights.