ML Engineer

📋 Job Overview

We seek a Machine Learning (ML) Software Engineer to help us deliver for our clients and invest in our platform. You’ll be one of the first full-time ML engineers and will have an enormous impact on all aspects of what we do.

🏢 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 (Puget Sound Energy). We’re a public benefit corporation, founded in 2024, and have been profitable from the beginning (bootstrapped with consulting).

🎯 The Role

You will work on AI projects in client engagements and, over time, internal platform capabilities. The role is initially 70% client delivery and 30% platform development, shifting to 50/50 over the first year. Some of our client projects are enterprise-style, and some are fast innovation cycles. Over time, we’ll be investing more in our own platform to accelerate client value.

✅ Key Responsibilities

• Turn ambiguous client problems into shipping code.
• Drive projects from discovery to deployment
• Collaborate with client and internal project teams
• Design and write clean, scalable code at appropriate quality standards (sometimes “right”, and sometimes “right now”).
• Design, integrate, and productionize ML solutions including predictive models, GenAI systems, physics-informed ML, and digital twins.
• Collaborate with domain experts in energy, real estate, and climate to translate business needs into ML solutions
• Advocate for engineering best practices and positive dev culture

📌 Required Qualifications

• 5+ years building and deploying ML systems in production environments
• Expert-level Python and experience with PyTorch / TensorFlow
• Deep expertise in at least one domain: NLP, Computer Vision, Time-Series, or Reinforcement Learning
• Generative AI and LLM-related capabilities (e.g., prompt engineering, RAG, fine-tuning, LangChain, model evaluation tooling)
• MLOps and infrastructure automation (e.g., CI/CD for ML, Docker, Kubernetes, Terraform, MLflow, Kubeflow)
• Strong engineering fundamentals: system design, scalability, testing, and monitoring
• Track record of translating ambiguous business problems into production ML solutions
• Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)
• Champion for quality (model validation, reproducibility, monitoring, bias/variance checks)

⭐ Desirable Experience

• Experience in both startup and enterprise environments
• Past work in energy, real estate, utilities, climate, or related fields
• 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)

🎁 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.

🛂 Visa & Eligibility

Remote, but only USA/Canada
Must be willing to travel to the Seattle area for the final interview and travel 2x/year for company summits
Additionally, depending on location, candidates can expect to spend 10-20% of their time working onsite with clients.
Applicants must be currently authorized to work in the United States on a full-time basis.
We are currently unable to sponsor or take over sponsorship of employment visas.