Liquid AI builds efficient Liquid Foundation Models (LFMs) for on-device, edge, and cloud AI with low latency, privacy, and hardware-aware deployment.
About the role
Our inference stack is central to everything we ship. You'll be a core part of the team responsible for the engine layer that runs our models in production and in partner environments, and for the benchmarking infrastructure we use to evaluate our own work and verify what partners bring to us. Day to day, that means working closely with research and product, but also directly with external engineering teams.
What they're looking for
- Hands-on experience with at least one inference framework like llama.cpp, ONNX Runtime, or MLX, going beyond basic usage into 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
More about this role
Role: Member Of Technical Staff, Infrastructure
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
Our inference stack is central to everything we ship. You'll be a core part of the team responsible for the engine layer that runs our models in production and in partner environments, and for the benchmarking infrastructure we use to evaluate our own work and verify what partners bring to us. Day to day, that means working closely with research and product, but also directly with external engineering teams.
Can pick up unfamiliar tools quickly and knows how to assess whether they're worth using.
Designs AI benchmarks and holds methodology to a high standard.
Cares about inference details, understands the tradeoffs, and checks what changed across the board before calling something done.
Doesn’t consider a model port finished until...
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