ML​/Data Engineer - AI Lab

STATIONED embeds engineers directly inside companies to help them get real value out of their data and AI investments — not theoretical, not experimental, but production-grade work that moves a business metric. As an ML/Data Engineer, you will work across a portfolio of companies, get inside their data, and build the pipelines, models, and systems that make AI actually work in practice.

You are not here to write a report about what they should do. You are here to build it.

• Can walk into a company, understand their data landscape fast, and identify where ML or AI creates real leverage
• Are comfortable owning the full stack — data pipelines, model integration, evaluation, and delivery — without a large team behind you
• Already use AI and LLM tools in your own workflow and think about how to augment ML systems with them
• Thrive moving across different industries, data types, and problem shapes back to back
• Can communicate clearly with non-technical stakeholders about what the data says and what it can and cannot do
• Would rather ship a working model that helps someone than pursue the theoretically optimal one indefinitely

• Need months of data access and ramp time before you can produce anything
• Are only comfortable working on well-structured, clean datasets handed to you by someone else
• Want to research and experiment without a delivery expectation
• Are looking for a big-company job with big-company structure and specialization

• Strong hands-on experience with data pipelines, ETL, and working with messy real-world data — you know how to get it into shape fast
• Experience integrating ML models or LLMs into production systems that real users or workflows depend on
• Fluency with Python and the core ML/data stack: you know your way around pandas, SQL, and at least one ML framework without needing to look everything up
• Ability to scope and size a data or ML problem quickly — you can tell early when something is feasible and when it is not
• Client-facing confidence: you can run a data discovery conversation, ask the right questions, and leave knowing what to build
• You use AI tools as a genuine multiplier in your own work — agents, LLM APIs, coding assistants — not just as a novelty

• Experience with vector databases, embeddings, or RAG architectures in a production context
• Background working across multiple industries or company types — healthcare, fintech, consumer, logistics
• Experience fine-tuning or evaluating LLMs for specific business use cases
• Public builds, repos, notebooks, or writing that shows how you think about data and models

We are an equal opportunity employer, all qualified applicants will receive consideration for employment without regard to race, color, religion, ancestry, national origin, sex, sexual orientation, gender identity or expression, place of birth, crime victim status, age, veteran status, or disability (or any other classification protected by law).