Member of Technical Staff - Inference Systems
Builds and maintains the inference engine layer for AI models, designing benchmarking suites and validating partner solutions across hardware targets (data centers/edge). Core focus: performance, correctness, and reproducibility of model deployments using frameworks like llama.cpp and ONNX.
- Compensation
- Not disclosed
- Location
- Boston
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Hands-on experience with inference frameworks like llama.cpp, ONNX Runtime, or MLX (internals/modification)
- Experience designing benchmarking pipelines (methodology, validation, reproducibility)
- Strong C++ and Python in performance-sensitive contexts
- Solid understanding of inference fundamentals (quantization, decoding, memory layout)
Nice to have
- Experience porting models across runtimes and verifying numerical correctness
- Prior work with external partners in technical validation/evaluation
- Familiarity with edge inference targets and constraints
Full description
About Liquid AI
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.
The Opportunity
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 We're Looking For
We need someone who:
- 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 you can prove the outputs are correct.
The Work
- 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.
Desired ExperienceMust-have:
- 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.
Nice-to-have:
- Experience porting models across runtimes and verifying numerical correctness.