Machine Learning Intern

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.

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

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.

• 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.
  

• 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