ML Engineer (Senior)

Job description

MACHINE LEARNING ENGINEER (SENIOR) 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). 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 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. You'll join a company founded by experienced, successful veterans in climate and AI. The team brings exceptional expertise from Microsoft, AI2, ARM, acquired startups, and even a major utility, including PhDs, published ML researchers, and former industry executives. 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. What you will do You will work on AI projects in client engagements and, over time, internal platform capabilities. You will: - 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 Core Qualifications - Technical and foundational - 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) 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) 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 - 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. Next steps If this job sounds like a great fit, we’d love to hear from you. If you feel aligned with the company but don’t check ALL of these boxes, we’d still love to hear from you!