Machine Learning Research Intern, Audio

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