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Posted 4 months ago

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ML Engineer, Audio

New York CityOn-siteFull-time

AI Summary

Machine learning engineer responsible for developing, evaluating, and deploying audio models for a conversational computer, spanning cloud and on-device settings; collaborates with ML/AI engineers, infrastructure, designers, and cofounders.

About this role

About Sandbar

Sandbar is an interface company in New York City. We aim to augment individuals in an agentic world—to think, act, and move freely. Our team has built SW, ML, and HW products across Meta, CTRL-labs, Google, Apple, Fitbit, Peloton, and Equinox.

Our first product, Stream, is a new human-agent interface—a private voice ring and conversational computer. Stream has been featured in WSJ, Bloomberg, & Wired, and begins shipping in Summer '26.

Join us in creating a new interface for self augmentation.

About

We’re looking for a machine learning engineer to help build Stream, a new conversational computer. As a machine learning engineer, you will develop a system which spans voice, memory, and agentic control. This role is perfect for someone cares deeply about real-world ML deployment and human-in-the-loop agentic interactions.

Responsibilities

  • Develop, evaluate, and deploy audio models spanning cloud models to resource-constrained on-device settings

  • Optimize inference pipelines for latency, reliability, and concurrency

  • Work closely with other ML/AI engineers, infrastructure engineers, designers, and cofounders on existing and future products

Qualifications

  • 4+ years in machine learning experience

  • Experience shipping ML-based products is required

  • Experience developing audio / voice models is required

  • Passion for human-computer interaction

FTE Benefits

  • Health, vision, and dental benefits

  • Unlimited PTO and sick time

  • Early stage equity

Skills

Audio ModelsCloud MLConcurrencyData PipelinesEdge DeploymentExperimental DesignHuman-in-the-loopInference OptimizationLatency OptimizationMemory SystemsMLOpsModel DeploymentNatural Language ProcessingOn-device MLR&D CollaborationReal-world ML DeploymentSignal ProcessingSpeech RecognitionVoice InterfaceVoice Models

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