ML Engineer (Senior/Staff)

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.

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.

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  Own architecture for inference serving and GPU scheduling — Kubernetes operators, autoscaling, and dynamic capacity across vLLM/SGLang deployments on cloud and customer-managed infrastructure.

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  Design and calibrate eval systems for model, prompt, and agent changes, including golden datasets, LLM-as-judge pipelines, and regression gates wired into CI.

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  Advise on cost-aware model routing and cascading decisions, balancing latency, cost, and quality across providers and model tiers.

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  Apply physics-informed ML and enterprise AI expertise to the hardest client and platform problems, drawing on the team's research depth.

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  Set technical standards for ML infrastructure and evaluation practice across the org, and mentor engineers working in this space.

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  Partner closely with the inference platform, gateway, and evals-focused engineers to keep architecture coherent as the platform grows.

• 3+ years of experience with ML infrastructure and inference serving — vLLM, SGLang, TensorRT-LLM, or comparable systems — at production scale.

• Strong background in evaluation and reliability engineering for ML/LLM systems, or the seniority to build this practice from scratch.

• Solid Kubernetes experience, ideally including GPU-specific scheduling constraints (node pools, autoscaling under GPU bottlenecks).

• A track record of technical leadership at a staff or senior level — setting direction, not just executing tickets.

• Research fluency is a plus (PhD, publications, or equivalent depth) given the technical bar of our existing ML team, though this is an infrastructure-and-systems role first.

• Advanced ML/AI frameworks and techniques (e.g., PyTorch Lightning, JAX, HuggingFace, ONNX optimizations)

• Lower-level or performance-focused languages for ML acceleration (e.g., C++, Rust, CUDA)

• Large-scale data and distributed training paradigms (e.g., Spark, Ray, Horovod, Dask)

• Advanced data infrastructure (e.g., vector/graph databases, feature stores, data lakes)

• Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.

• Competitive early-stage startup compensation (based on capabilities, experience, and location)

• Bonus eligibility

• Health insurance with meaningful coverage for dependents

• Flexible paid time off

• Equity

• Fully remote culture with a cluster of teammates in Seattle

• Must be willing to travel to Seattle area for final interview and travel 2x/year for company summits

If this job sounds like a great fit but don’t check ALL of these qualification boxes, we’d still love to hear from you!