Machine Learning Engineer

Your day to day

A core part of our product is an increasingly advanced AI-enabled system and you lovingly own and push this system forward. Most days, your work centers around designing and prototyping our AI agent functionality, which is a blend of language modeling, prompt engineering, network science, and good old fashioned counting numbers. Increasingly, these are topics that AI itself is getting better at analyzing, and so you heavily use AI as an outside expert in your development process.

Your work is heavy on application development, deployment, and working closely with the rest of product engineering to define features and capabilities. These are the parts of our organization that will make and break us – the better we’re able to coordinate a boatload of development activity and understand the right problems to solve, the faster we’ll move. That said, the work requires sharp analytical and mathematical skills, if not a concrete history of building AI systems.

You also strongly believe that components that used to be imperatively coded are now being learned instead. At Filament, your role is pushing this a bit further, allowing AI to control its own functionality when this presents unique and innovative capabilities to the user. This also means you are an ambassador for integrating this new style of programming into our product, providing our internal developers with the power of these new tools, and building the environment that allows us to analyze our product and discover valuable new features.

Signals you might fit well with this job

These are not hard and fast requirements. Some of the best people we’ve ever hired have not ‘fit the profile’ and we prize above all the curiosity and ability to learn fast and adapt. Think of these as useful guidelines to understand more about the job itself.

  • Coding Proficiency: Strong Python skills. Experience with typed languages (Rust, Typescript, Julia) a plus.

  • Production Experience: Brought ML into real-world applications (user-facing or data pipelines).

  • Pragmatic Approach: You can balance simplicity, robustness, and complexity effectively.

  • Quantitative Mindset: Sharp analytical skills. Formal training is less important than the ability to critique, grasp, and apply fundamental mathematical concepts.

  • Resourceful Learner: Quickly research and apply new concepts using online resources and AI tools. Comfortable with "learning in public."

  • Driven & Curious: Passionate about learning and applying sharp ideas to product.