Senior AI Research Engineer

About this role
We are looking for a builder who thinks like a researcher.
We don't care about the logos on your resume; we care about the quality of your code and the clarity of your thinking. We are looking for the kind of person who reads a paper in the morning and has a working implementation by the evening.

What You’ll Do
This is not a traditional "research" role where you write papers in a silo. This is an engineering role where the medium is AI. You will be working on the frontier, taking bleeding-edge concepts and wrestling them into products that actually work and solve real problems.
You will bridge the gap between theory and reality. You will fail often, but you will learn faster than anyone else in the room.

Examples of Every Day Work

• Prototype and iterate on new AI-native product features daily.
• Read and implement relevant research papers, adapting them to our specific constraints.
• Design and run evaluations to measure the performance of our models and agents.
• Debug complex interactions between models, tools, and infrastructure.
• Push code that impacts users immediately.

What We’re Looking For

• A Builder at Heart: You have a GitHub profile full of experiments, hacks, and tools. You build things because you can't help it.
• Deeply Curious: You don't just use libraries; you want to know how they work under the hood. You read arXiv papers for fun, but you’re pragmatic enough to know when to just ship it.
• High Slope: You might be "junior" in years, but your trajectory is vertical. You absorb new information instantly and are constantly upskilling.
• Resilient: You know that working with LLMs and agents involves non-deterministic chaos. You don't get frustrated when things break; you get curious.
• Creative: You approach problems laterally. When the standard fine-tuning approach fails, you invent a new prompting strategy or architecture hack to get it done.

Tech Requirements

• Strong proficiency in Python and modern AI frameworks (PyTorch, JAX, etc.).
• Experience with LLMs, RAG pipelines, or agentic workflows (internships and side projects count).
• A solid grasp of software engineering fundamentals—you write code that is clean, readable, and testable.