Member of Technical Staff - Research Engineer, Omni

Sourced from jobcedar.com

Liquid AI, an MIT CSAIL spinout building efficient general-purpose AI systems, is hiring a Research Engineer for its Omni multimodal team in Boston. This is an end-to-end model training role spanning open research, data pipelines, pre- and post-training, evals, and inference/deployment for a shipped vision-and-audio multimodal model. You will work directly with the Omni lead and vision/audio teams, with the Chief Science Officer close to the research. The team is small and open on level, considering both experienced engineers and strong early-career candidates.

• Run multimodal training experiments end to end, including pre- and post-training, and fold results into the next Omni release
• Contribute to open research directions such as multimodal MoE and token-efficient encoders
• Own multimodal data work: pipelines, curation, and data decisions feeding each release
• Harden research-grade experiment code into maintainable infrastructure in the team repo
• Consolidate Omni's training infrastructure into one codebase and library with defended distributed-training choices
• Own inference and deployment for Omni models and work across vision and audio teams

• Hands-on experience training ML models in at least one non-text modality (vision most likely)
• Foundational understanding of how multimodal training works below the research layer, explainable from past work
• Comfortable sitting between research and engineering: run experiments and harden the code behind them
• Able to take training infrastructure and inference from experiment code to production-ready and willing to own that path

• Audio modality and speech modeling experience
• Prior inference or deployment experience, including on-device inference
• Exposure to multimodal MoE or encoder token-efficiency research
• Publication record or academic research background