FluidStack
Machine Learning Engineer
About this role
Fluidstack is seeking a Machine Learning Engineer to build and deploy ML and LLM systems that optimize internal operations, including forecasting, risk detection, and document processing. You'll own models end-to-end from conception through production, and develop agentic systems with proper safeguards and audit trails that integrate into company workflows.
What you'll do
- Design and deploy ML/LLM systems for operational use cases like timeline forecasting and vendor document extraction
- Own the complete model lifecycle from problem definition and data preparation through deployment and production iteration
- Build and maintain evaluation frameworks that validate model quality before and after production deployment
- Develop agentic systems with guardrails, authorization controls, and audit capabilities for autonomous operations
- Collaborate with data engineering and product teams to integrate predictions into existing tools and workflows
- Ship production-quality code and iterate rapidly based on real-world performance
What they're looking for
- Machine learning model development and deployment to production
- LLM APIs, fine-tuning, and retrieval system implementation
- Evaluation harness design and model quality assessment
- Production-quality Python or similar programming languages
- AI coding tools and modern development workflows
- Agentic frameworks and workflow automation
- Forecasting and scheduling optimization (preferred)
- Document extraction and NLP at scale (preferred)
Opens the official application on the employer’s site. No login required.
FluidStack
FluidStack builds AI infrastructure at scale, developing data centers and warehouse operations designed to handle gigawatt-capacity compute deployment. The company is hiring for warehouse engineers, data center operations specialists, product engineers, and people leaders to support rapid infrastructure expansion across multiple sites.
- Website
- fluidstack.io
Likely interview questions
- Walk us through an ML or LLM feature you shipped to production—what was the problem, how did you approach it, and how did you measure success?
- Describe an evaluation harness you built. How did it help you catch issues before users encountered them?