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