Machine Learning Engineer

About the company

Sandbar builds the Stream smart ring and companion app that let users capture and interact with AI-powered voice notes on the go.

About this role

About Sandbar Sandbar is an interface company in New York City. We aim to augment individuals so we can each think, act, and move more 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 self extension—a private voice ring and conversational interface. Stream has been featured in WSJ, Bloomberg, & Wired, and begins shipping in Summer '26. Join us in creating technology that extends human thinking. RESPONSIBILITIES: This role involves developing software and machine learning algorithms for use in human computer interface products. This includes designing system architectures involving large language models for conversational, information retrieval, and digital automation tasks. This also includes utilizing data science and statistical methods to analyze and optimize machine learning model performance across tasks. Lastly, this includes optimizing the aforementioned systems for latency, reliability, and cost. Telecommuting permitted. 3x/week in office required. ‍REQUIREMENTS: Requires a bachelor’s degree in computer science, engineering, data science or machine learning plus 2 years of experience as a machine learning engineer. Must also possess: 2 years building & deploying ML-based applications, including data curation and model training; 2 years of experience researching and developing applications based on large language models such as OpenAI GPT models or Anthropic Claude models, and open source models such as Meta Llama models; 2 years of experience in quantitative analysis based on data science; 2 years of experience in Python programming language; 2 years of experience building real-time applications utilizing ML models, such as real-time voice conversation via speech-to-text models, real-time image processing utilizing vision-language models, or other real-time ML-based systems in a professional context for internal tools or external products; 1 year of experience with AWS or GCP; 1 year of experience with Docker, TorchServe, AWS Lambda, or SageMaker; 1 year of experience developing retrieval augmented generation systems to improve the accuracy of information retrieval in applications utilizing large language models in a professional context for internal tools or external products; and prior experience in at least 1 startup-stage company (founding engineer to Series A company). Must be able to successfully complete competency-based interviews FTE Benefits - Health, vision, and dental benefits - Company-sponsored 401(k) - Unlimited PTO and sick time - Early stage equity