Senior Machine Learning Engineer

Fuel Talent; Seattle (WA); Hybrid; USD 160,000 - 230,000; Full time

Job summary

Fuel Talent is partnering with a mission-driven startup in Seattle to hire a Sr. Machine Learning Engineer. You will own model performance in production, fine-tune foundation models, and build retrieval-augmented systems for enterprise use.

This hybrid role offers a base salary of $160K–$230K plus equity and benefits. You will work with Python, PyTorch, and modern ML tooling, shaping experiments from data prep to deployment while balancing latency and cost.

Qualifications

• 4+ years of ML or applied AI experience with production models.
• Strong Python and hands-on experience with PyTorch or Hugging Face.
• Practical experience with LLMs: fine-tuning, RAG, prompts, agents, or evals.
• Experience designing evaluation pipelines to measure model quality.
• Familiarity with containerization and cloud-based deployment.

Responsibilities

• Fine-tune, adapt, and optimize LLMs and foundation models for domain-specific use.
• Design and build agentic workflows and retrieval-augmented systems from prototype to production.
• Build rigorous evaluation and benchmarking systems to prove improvements.
• Own model performance end-to-end: data prep, experiments, deployment, monitoring.
• Collaborate with engineering and product to translate business problems into ML solutions.
• Ship models reliably with latency and cost considerations.

Skills

Python

LLMs

Model evaluation

Production ML

Latency optimization

Tools

PyTorch

Hugging Face

Docker

Kubernetes

Terraform

Job description

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.

About the role

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.

What you'll do

• 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

What we're looking for

• 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

Nice to have

• 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)