Job description

ABOUT PHONIC Phonic is an audio research lab focused on powering the most realistic, human-like voice AI conversations. We’ve rethought the entire stack in pursuit of this goal, from models to product, to create voice agents that feel like they truly understand you, respond emotionally, and perform agentic tasks with frontier intelligence. Our customers build voice-native AI products in industries like customer support, healthcare, logistics, and recruiting. We’ve grown ~20x since the start of this year, and we’ve raised over $30M from tier-1 VCs, including First Round Capital, Lux Capital, and Felicis. About the Team Phonic has an extremely talent-dense and close-knit team of top-tier AI researchers, international olympiad medalists, and former founders. We work with high trust and keep raising the bar on how we deliver world-class research and product experiences. Everyone takes ownership of what they do and isn’t afraid to dive headfirst into new problems. We’re fully in person in our SF office. THE ROLE As a Research Intern at Phonic, you'll work directly on the core problems that make Phonic's voice AI feel genuinely human. You'll own a research direction end-to-end — from identifying the right problem to designing experiments, developing novel methods, and seeing results through to production impact. This isn't a role focused on incremental improvements; we're looking for someone with the taste to identify what matters, the rigor to pursue it correctly, and the drive to ship it. You'll be fully in-person in our SF office. WHAT YOU'LL DO - Identify high-leverage research problems across the voice AI stack from audio understanding to audio output, and take full ownership of driving them forward - Design and run rigorous experiments that analyze architectural trade-offs to understand how design choices influence a model’s scalability, latency, and quality - Curate massive training datasets and execute rigorous experiments to determine exactly how data quality shapes model behavior and performance - Work directly with research scientists and engineers to move fast from prototype to production - Build the training pipelines, evaluation frameworks, and tooling that let us experiment and iterate quickly WHAT YOU'LL BRING - A track record of original work: you've found a real problem, developed an approach, and seen it through - Proficiency in PyTorch (or JAX), and the ability to implement models cleanly from papers - Fluency in the math, probability, optimization, and linear algebra underlying model behavior - You move fluidly between ideas and implementation; you don't just think about problems, you build things - Clear, precise written and verbal communication NICE TO HAVE - Research experience in speech, audio, or language modeling (ASR, TTS, LLMs, codec models) - Familiarity with generative modeling techniques: diffusion, flow matching, or autoregressive models - Experience with RLHF or preference optimization - Competitive programming or olympiad background - Publications or preprints at venues like NeurIPS, ICML, ICLR, Interspeech, ICASSP, or ACL Benefits - 💸 Top-tier compensation: in order to get the best talent, we provide salary and equity that recognize your skillset - 🥗 Meals: free breakfast, lunch, and dinner provided in the office - 🤝 We have regular off-sites and team celebrations

Requirements

Must-haves

Identify high-leverage research problems across the voice AI stack from audio understanding to audio output, and take full ownership of driving them forward
Design and run rigorous experiments that analyze architectural trade-offs to understand how design choices influence a model’s scalability, latency, and quality
Curate massive training datasets and execute rigorous experiments to determine exactly how data quality shapes model behavior and performance
Work directly with research scientists and engineers to move fast from prototype to production
Build the training pipelines, evaluation frameworks, and tooling that let us experiment and iterate quickly
A track record of original work: you've found a real problem, developed an approach, and seen it through
Proficiency in PyTorch (or JAX), and the ability to implement models cleanly from papers
Fluency in the math, probability, optimization, and linear algebra underlying model behavior
You move fluidly between ideas and implementation; you don't just think about problems, you build things
Clear, precise written and verbal communication

Nice-to-haves

Research experience in speech, audio, or language modeling (ASR, TTS, LLMs, codec models)
Familiarity with generative modeling techniques: diffusion, flow matching, or autoregressive models
Experience with RLHF or preference optimization
Competitive programming or olympiad background
Publications or preprints at venues like NeurIPS, ICML, ICLR, Interspeech, ICASSP, or ACL

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: ashby
Effort: quick