Research Engineer

About the role

ABOUT PHONIC

Phonic is the speech-to-speech model company. Our mission is to make speaking with agents delightful, and we believe that requires models that hear and answer in native audio. To make that possible in production, we work on every layer of the stack: data, pre-training, post-training, kernels, and infrastructure. We’ve raised over $30M from top-tier investors, including First Round, Lux Capital, and Felicis. Our models power voice agents on six continents for companies like Ashby, Maven AGI, and a Fortune 50 IT infrastructure company, handling everything from customer support and patient intake to recruiting interviews and dispatch.

ABOUT THE TEAM

We’re a small team of researchers and engineers from MIT, Stanford, CMU, MosaicML, Citadel, and Genesis Therapeutics, including former founders, an IMO medalist, and an International Linguistics Olympiad medalist. We work in person in San Francisco five days a week. Everyone here is unusually obsessed with our customers: we’ll happily spend hours perfecting the way our model reasons while it talks, or the way it makes a caller feel heard, going as deep into the stack as it takes.

THE ROLE

As a Research Engineer at Phonic, you'll sit at the intersection of cutting-edge ML research and production engineering, working directly on the core systems that make Phonic's voice AI feel genuinely human. You'll design, train, and iterate on models across the voice stack (speech, audio, language, and beyond), while also building the infrastructure and tooling needed to move fast from research idea to deployed product. This is a role for someone who thrives in ambiguity, moves with urgency, and takes full ownership of problems. You'll work closely with researchers and product engineers in a high trust, in-person environment in our SF office to push the frontier of what voice AI can do.

WHAT YOU’LL DO

- Design, implement, and iterate on models across the voice stack from audio and speech to language and beyond

- Build the training pipelines, evaluation frameworks, and tooling that let us experiment and iterate quickly

- Translate research results into production-grade systems alongside our engineering team

- Form hypotheses, chase down interesting results, and take ownership over the problems you work on

WHAT YOU’LL BRING

- Hands-on experience in ML research or research engineering, industry or PhD-level academia

- Proficiency in Python and PyTorch (or JAX), and the ability to implement models cleanly from papers

- Experience running ML experiments end-to-end: data processing, training, evaluation, and iteration

- Comfort in fast-moving, ambiguous environments - defining the problem is part of the job

- The ability to clearly explain what you tried, what worked, and why

NICE TO HAVE

- Research experience in speech, audio, or language modeling (ASR, TTS, LLMs, codec models)