Machine Learning Research Intern, Audio

About this role

THE ROLE: MACHINE LEARNING RESEARCH INTERN, AUDIO

As a Research Intern at Bland, you will own a focused research project across our voice stack: speech-to-text, large language models, neural audio codecs, or text-to-speech. You will work alongside our research team on the same problems they are working on, not on a side track built to keep interns busy.

We scope internships around a single meaningful question that can be answered in the time you have. The goal is a result worth shipping, publishing, or both. Interns here regularly see their work reach production systems handling millions of calls.

WHAT YOU WILL DO

Own a research question end to end

- Take one well-scoped problem from literature review through implementation, experimentation, and results.

- Design ablations that isolate what actually caused an improvement.

- Present your findings to the research team and defend the methodology.

Work on real systems

- Train and evaluate models on large-scale, real-world telephony audio, including the accents, noise, and artifacts that make production speech hard.

- Use our distributed GPU infrastructure rather than toy-scale setups.

- Where the result warrants it, work with engineers to move it toward production.

Choose your depth Depending on your background and interests, your project may focus on:

- Expressive and controllable text-to-speech, including prosody and emotion modeling

- Neural audio codecs and discrete or continuous speech representations

- ASR robustness for telephony, accents, and code switching

- Real-time and streaming inference under latency constraints

- Full-duplex conversation and turn-taking dynamics

WHAT MAKES YOU A GREAT FIT

Research foundations

- Currently pursuing a MS or PhD in ML, CS, EE, or a related field, or equivalent research experience.

- Comfortable reading a paper and reimplementing it without hand-holding.

- Experience with self-supervised, generative, or multimodal modeling.

Audio or speech grounding

- Hands-on work with speech or audio models, whether TTS, ASR, codecs, or audio representation learning.

- Strong intuition for audio quality and what makes synthetic speech sound wrong.

- Prior publications or open source contributions in speech or language AI are a strong signal, though not required.

Engineering ability

- Fluent in PyTorch and comfortable in a real codebase.

- Able to run your own experiments on GPU clusters without waiting to be unblocked.

HOW YOU SHOW UP

- You identify the single experiment that validates an idea in days, not months.

- You measure everything and let data drive decisions.

- You are honest about negative results, because they are how we narrow the search.

- You are obsessed with making voice agents sound truly human.

- You use AI tools aggressively to amplify your own impact.

BENEFITS

- Competitive intern compensation

- Mentorship from researchers working on frontier voice AI

- Every tool you need to succeed

- Beautiful office in Levi's Plaza, SF with rooftop views

- A real shot at a return offer

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