Member of Technical Staff - Research Engineer, Omni

The role

You will run multimodal model training end to end for Omni, from research and data through training, evaluation, inference and deployment. You will work with the Omni lead and the vision and audio teams on the next release.

What you will do

• Run multimodal training experiments end to end, pre- and post-training, and fold the results into the next Omni release.
• Contribute to open research directions such as multimodal MoE and token efficiency for multimodal encoders.
• Share the multimodal data work: pipelines, curation, and the data decisions that feed each release.
• Take research-grade experiment code and harden it into maintainable infrastructure in the team repo.
• Help consolidate Omni's training infrastructure after the latest release: one codebase, one library, and distributed-training choices made and defended.
• Own inference and deployment for Omni models, and work across the vision and audio teams on the connective tissue between data, infra, training, and evals.

Required qualifications

• Hands-on experience training ML models in at least one non-text modality (vision is the most likely path; audio is rare and can be learned here).
• A foundational understanding of how multimodal training works, below the research layer, and the ability to explain it from your own past work.
• Comfortable sitting between research and engineering: you run experiments and harden the code behind them.
• Able to take training infrastructure and inference from experiment code to something maintained and production-ready, and willing to own that path.
• Owns a task through to done: takes a loosely defined problem, decides what good looks like, and finishes it well without being chased.
• Research curiosity: goes deep on why a method works and asks about directions the team has not taken yet.
• Cares about infrastructure quality: reviews and hardens code before it lands, including code an AI wrote.

Nice to have

• Audio modality and speech modeling experience.
• Prior inference or deployment experience, including on-device inference.
• Exposure to multimodal MoE or encoder token-efficiency research.
• A publication record or academic research background; what matters is whether you understand what you built.
• Having consolidated or rebuilt a training stack before, and your own opinions on libraries and distributed paradigms.

Benefits

• Equity, so you share in what the company builds.
• Competitive base salary.
• Unlimited PTO.
• Company-wide Refill Days, long weekends when the whole company rests together.
• Comprehensive health coverage, with details varying by location.
• Paid leave for pregnancy, parenting, medical needs, and caring for family.

Before you apply

• Final offers reflect skills, experience, and location.
• Experienced engineers and strong early-career candidates are both considered.

About the company

Liquid AI was spun out of MIT CSAIL. It builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, and partners with enterprises in consumer electronics, automotive, life sciences, and financial services.