ML Engineer (Senior/Staff)

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About AZX Our mission is to accelerate positive impact in critical industries through AI transformation. We specialize in physics-informed ML and enterprise AI solutions that directly address climate and sustainability challenges. We’re growing quickly and already work with category-leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe) and utilities. We’re a public benefit corporation, founded in 2024, and have been profitable from inception. We work on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics. We’re building our company for long-term success and aim to create the ultimate place to work for those passionate about AI and making a positive impact. About the Role We're looking for an ML Engineer to own the technical backbone of how AZX serves and evaluates models at scale. This is a high-leverage IC role spanning our inference platform — GPU scheduling, autoscaling, and serving infrastructure for vLLM/SGLang across cloud and customer-managed clusters — and the evaluation systems that tell us whether model, prompt, and agent changes actually make things better. You'll create technical direction for how AZX serves models reliably. This role suits someone who wants architectural ownership over hard ML infrastructure problems, paired with the judgment to build the guardrails that let the rest of the team move fast safely. Responsibilities Own architecture for inference serving and GPU scheduling — Kubernetes operators, autoscaling, and dynamic capacity across vLLM/SGLang deployments on cloud and customer-managed infrastructure. Design and calibrate eval systems for model, prompt, and agent changes, including golden datasets, LLM-as-judge pipelines, and regression gates wired into CI. Advise on cost-aware model routing and cascading decisions, balancing latency, cost, and quality across providers and model tiers. Apply physics-informed ML and enterprise AI expertise to the hardest client and platfo

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