Member of Technical Staff - Inference Systems
Liquid AI · Boston
CHECKED 23H AGO
Frontier labremote
At a glance
Highlights
- Build benchmarks for inference performance and model quality across hardware.
- Port models to new platforms and prove outputs are correct end-to-end.
- Run partner verifications and deliver clear, reproducible findings.
What they offer
- Equity in a unicorn-stage company
- 100% medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO plus company-wide Refill Days
Ideal candidate
A rigorous inference engineer who obsesses over correctness and reproducibility, moves fast with unfamiliar tools, and enjoys validating systems with external partners.
Requirements
- Hands-on experience with at least one inference framework like llama.cpp, ONNX Runtime, or MLX, including internals and modification
- Experience designing and building benchmarking pipelines, including methodology, validation, and reproducibility
- Strong C++ and Python in performance-sensitive contexts
- Solid understanding of inference fundamentals: quantization, decoding strategies, memory layout, and how they interact
Lab profile
About Liquid AI
MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.
What they build
- Liquid Foundation Models
- LFM2.5-Audio
Known for
- Efficiency-first foundation models
- Data center to on-device deployment
- Low latency with minimal memory usage
- Liquid Neural Networks architecture
Key facts
- Spun out of MIT CSAIL