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.
About
We’re looking for a Lead ML Engineer to build Stream, a new conversational interface. As an ML Lead, you will architect, build, and deploy a new interface which spans agentic control, multimodal interactions, and information retrieval. This role is perfect for someone who cares deeply about real-world ML deployment and human-in-the-loop agentic interactions.
Responsibilities
Lead architecture, strategy, and tradeoffs for our ML-based products
Design and improve user-facing model-based experiences, including agentic control, conversational interactions, and information retrieval.
Develop infrastructure to rapidly develop, evaluate, and deploy models
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
6+ years in machine learning experience
Experience shipping ML-based products is required
Experience in large-language model evaluation and post-training is required
Experience in audio / voice ML is a plus
Comfortable writing production-grade software
Passion for human-computer interaction
Experience setting technical direction and driving execution with multidisciplinary teams
FTE Benefits
Health, vision, and dental benefits
Company-sponsored 401(k)
Unlimited PTO and sick time
Early stage equity
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Sandbar builds performant anti-money laundering (AML) software to more accurately identify illegal activity. Criminals launder an estimated $4 Trillion every year, yet less than 1% of this is caught. To provide better results, we ingest millions of messy data points, standardize that data, and run a complex suite of proprietary calculations designed to detect suspicious activity. Our product automatically monitors activity and empowers risk teams by putting necessary information at their fingertips. We provide information accessibility, build intuitive workflows, and allow for custom processing. Doing better can help cut off funding for kidnappers, human traffickers, terrorists, scammers, and other bad actors as well as reduce the effort and costs associated with developing and maintaining internal AML programs. We are a small but growing team that values creativity, collaboration, and innovation. We're backed by Lachy Groom, Abstract Ventures, BoxGroup, and over 40 other founders, early employees, and executives of Ramp, OpenAI, Stripe, Square, Plaid, and more.