Software/ML Engineer

We're assembling a group of senior software and ML engineers to work on one of the hardest open problems in AI today: how well can frontier models reason about LLM inference systems, GPU-level performance optimization, and model-serving architecture? You'll help design evaluation scenarios, write reference solutions, and grade model outputs on real systems-engineering problems - the same class of problems you likely work on day-to-day.
This is a research-and-evaluation role, not a traditional engineering job - you won't be shipping production code for us, you'll be defining what "correct" and "excellent" look like for AI models tackling the problems you already know deeply.
Responsibilities
What You'll Do Design realistic technical scenarios and problem sets in LLM inference optimization, GPU kernel design, and systems performance engineering
Required Qualifications
Bachelor's degree or higher in Computer Science, Computer Engineering, Electrical Engineering, or a related field
Strong, hands-on background in one or more of: GPU inference optimization, custom CUDA kernel development, model-serving systems (e.g., vLLM, TensorRT-LLM, SGLang), quantization, or optimizer design
Experience at a recognized technology or AI company, in a role with real systems-engineering ownership
Strong written communication - you'll be authoring technical explanations and feedback, not just code
Preferred Qualifications
Direct experience with SGLang, Mamba/Mamba2 architectures, or IBM Granite-family models
Open-source contributions to inference/serving frameworks (vLLM, SGLang, TensorRT-LLM, etc.)
Experience with distributed inference (tensor/expert parallelism, continuous batching, KV-cache optimization)
Why apply
Remote, contract, part-time
Flexible - work on your own schedule

Work arrangement
Yes