ML Engineer (Senior/Staff)

What you will do

• You will work on AI projects in client engagements and, over time, internal platform capabilities. You will:
• 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 platform problems, drawing on the team's research depth. • Set technical standards for ML infrastructure and evaluation practice across the org, and mentor engineers working in this space. • Partner closely with the inference platform, gateway, and evals-focused engineers to keep architecture coherent as the platform grows.

Core Qualifications - Technical and foundational

• 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.

Values and Culture Qualifications

• High emotional intelligence and a learning mindset
• Strong collaboration skills
• Enjoy others' success and a fun, positive environment.
• Comfortable making decisions in the face of ambiguity and course correcting as needed.

Bonus Qualifications (not required but a huge plus)

• Experience in both startup and enterprise environments
• Past work in energy, real estate, utilities, climate, or related fields
• Bonus if you have experience and passion in one or more of 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)
• 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)

Compensation & benefits

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

Logistics

Next steps