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 Audio team is building frontier speech-language models that handle STT, TTS, and speech-to-speech in a single architecture. This role sits at the center of applied audio model development, working directly with the technical lead to ship production systems that run on-device under real-time constraints. You will own critical workstreams across data pipelines, evaluation systems, and customer deployments.
What they're looking for
- Strong programming fundamentals with demonstrated ability to write clean, maintainable, production-grade code
- Experience building and shipping production ML systems beyond model training (data pipelines, evals, serving infrastructure)
- Proficiency in PyTorch and familiarity with distributed training frameworks (DeepSpeed, FSDP, or similar)
- Track record of collaborating effectively in shared codebases with high engineering standards
More about this role
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 Audio team is building frontier speech-language models that handle STT, TTS, and speech-to-speech in a single architecture. This role sits at the center of applied audio model development, working directly with the technical lead to ship production systems that run on-device under real-time constraints. You will own critical workstreams across data pipelines, evaluation systems, and customer deployments. If you want high ownership on rare technical problems in a small, elite team where your code ships, this is the role.
Builds first, theorizes later: You ship working systems, not just notebooks. Production-grade code is your default, not a stretch goal.
Owns outcomes end-to-end: From data pipelines to customer deployments, you take responsibility for the full stack without...
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