Staff Engineer, AI Inference & Benchmarking

Liquid-Ai; Cambridge (MA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive base salary with equity

Health premiums paid (medical, dental,

401(k) matching up to 4%

Unlimited PTO and Refill Days

Job summary

Liquid AI in Boston, MA is seeking a Member Of Technical Staff, Infrastructure to drive the inference stack from research to production. You will design benchmark suites, port models across runtimes, and verify correctness end-to-end with external partners.

The role emphasizes C++ and Python in performance-sensitive contexts, with hybrid work and a strong focus on quantization, memory layout, and evaluation methodology to ensure reliable, scalable AI systems.

Qualifications

• Hands-on 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.

Responsibilities

• Design and build benchmark suites that cover inference performance, model quality, and knowledge evaluation across different hardware targets.
• Run external partner verifications: evaluate their solutions against our benchmarks, identify gaps, and clearly deliver findings.
• Port models like LFM2 onto different runtimes and frameworks, and verify correctness end-to-end.
• Maintain and extend the inference engine layer built on llama.cpp, ONNX, and MLX as new model architectures emerge from research.
• Make benchmark results explainable and verifiable, so internal teams and partners can trust and reproduce them independently.

Skills

llama.cpp

ONNX Runtime

MLX

Benchmarking pipelines

C++

Python

Inference fundamentals

Job description

Liquid AI in Boston, MA is seeking a Member Of Technical Staff, Infrastructure to drive the inference stack from research to production. You will design benchmark suites, port models across runtimes, and verify correctness end-to-end with external partners.

The role emphasizes C++ and Python in performance-sensitive contexts, with hybrid work and a strong focus on quantization, memory layout, and evaluation methodology to ensure reliable, scalable AI systems.