Senior Machine Learning Engineer

Sr. Machine Learning Engineer | Startup; mission-driven AI-for-good

Hybrid 1x/week, Seattle

Compensation: $160K–230K base (pending leveling) + equity + benefits

Visas:
No; due to the government contracts associated with this role, we are only able to accept US Citizens or Green Card holders.

We are partnered with a public benefit corporation using AI to accelerate impact in critical industries: clean energy, decarbonization, climate risk, energy systems, and more. They're backed by leading AI and climate investors and are set to double the team over the next year.

We are looking for a Machine Learning Engineer to own how our client's models actually perform in production: fine-tuning and adapting foundation models, building the retrieval and agentic systems around them, and developing the evaluation infrastructure that proves what's working. This is a hands-on, high-leverage role for someone who lives in the loop of experiment, measure, and iterate, and who wants to see their models drive real outcomes for enterprise customers.

• Fine-tune, adapt, and optimize LLMs and other foundation models for domain-specific enterprise use cases
• Design and build agentic workflows and retrieval-augmented (RAG) systems, from prototype through production
• Build rigorous evaluation and benchmarking systems that measure whether model, prompt, and agent changes actually improve results
• Own model performance end to end: data preparation, experimentation, deployment, and monitoring in production
• Work with the serving stack (vLLM/SGLang, inference optimization) to ship models reliably and cost-effectively
• Partner with engineering and product to translate messy business problems into ML solutions

• 4+ years of ML or applied AI experience, with production models you've built, shipped, and owned
• Strong Python and hands-on experience with modern ML tooling (PyTorch, Hugging Face, or similar)
• Practical experience with LLMs: fine-tuning, RAG, prompt engineering, agents, or evals
• Experience designing evaluation or benchmarking pipelines that measure model quality
• Comfort moving from experimentation to production, with an eye on latency, cost, and reliability
• Familiarity with containerization and cloud-based model deployment

• Experience in both startup and enterprise environments
• Past work in energy, real estate, utilities, climate, or related fields
• Inference optimization (vLLM, SGLang, ONNX, quantization), vector or graph databases, or MLOps (CI/CD, Docker, Kubernetes, Terraform)
• Classical ML alongside LLMs (scikit-learn, XGBoost, forecasting, time-series)