Machine Learning Research Intern 2027

Phonic is a product and research lab focused on powering the most realistic, human-like voice AI conversations. We've re-thought 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 team includes top-tier AI researchers, international olympiad medalists, and former founders.

Our customers include companies that are building voice-native AI products in industries such as customer support, healthcare, and logistics. We have raised over $30M from tier 1 VCs.

Phonic has a very talent-dense and close-knit team. We collaborate with high trust and are constantly trying to improve how we work to deliver world-class research and product. Everyone takes ownership in what they do and they aren’t afraid to dive in headfirst into new problems. Our team includes top-tier AI researchers, international olympiad medalists, and former founders and we’re fully in-person in our SF office.

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.

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  Identify high-leverage research problems across the voice AI stack from audio understanding to audio output, and take full ownership of driving them forward

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  Design and run rigorous experiments that analyze architectural trade-offs to understand how design choices influence a model’s scalability, latency, and quality

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  Curate massive training datasets and execute rigorous experiments to determine exactly how data quality shapes model behavior and performance

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  Work directly with research scientists and engineers to move fast from prototype to production

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  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

• 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