Jobs

We’re hiring a Machine Learning Engineer to work on model optimization pipelines in tight collaboration with our systems engineers.
This is a hybrid research and engineering role, we expect the candidate to come up with new ideas, conduct experiments and build production-grade pipelines.
All of the work will be released as open-source.

What you’ll do:
You will work on applying post-training modifications to compress and accelerate the existing Large Language Models. The goal is to push the Pareto frontier across latency, accuracy and memory consumption for on-device inference engines.
The job involves analyzing the full inference stack, from hardware to software, looking for opportunities to remove bottlenecks and increase utilization, implementing model compression algorithms and training acceleration adapters.

What we’re looking for in a candidate:

• Solid knowledge of fundamentals of Machine Learning, Linear Algebra and Statistics
• Ability to write maintainable Python code
• Familiarity with LLM inference and standard LLM optimization techniques
• High-level undestanding of GPU architecture and programming model

Nice to have:

• Systems engineering experience, especially GPU/NPU programming
• Previous experience working with LLM inference
• Familiarity with JAX

Why join?
You’ll work on applied research that directly impacts how AI systems operate in real-world environments, not just benchmarks.
We are a fast-paced horizontal team with a lot of autonomy and trust.
We value technical depth and fast iteration.
Competitive compensation + meaningful equity.