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Bland

Machine Learning Research Intern, Audio

San FranciscofulltimeinternAdded today

About this role

Join Bland's research team as a Machine Learning Research Intern focused on audio and voice AI. You'll own a meaningful research project across their voice stack—from speech-to-text to text-to-speech—working on real production systems that handle millions of calls, with the goal of shipping, publishing, or both.

What you'll do

  • Own a focused research question from literature review through implementation, experimentation, and presentation
  • Design and run ablation studies to isolate what drives improvements
  • Train and evaluate models on large-scale, production telephony audio with real-world noise and accents
  • Work with distributed GPU infrastructure to scale beyond toy datasets
  • Collaborate with engineers to move validated results toward production systems
  • Present findings to the research team and defend methodology

What they're looking for

  • PyTorch and deep learning frameworks
  • Speech or audio model experience (TTS, ASR, codecs, representation learning)
  • Self-supervised, generative, or multimodal modeling
  • GPU cluster training and distributed computing
  • Research paper implementation and experimentation design
  • Audio quality assessment and intuition
  • Rapid prototyping and hypothesis validation
  • Data-driven analysis and negative result interpretation

Benefits

  • Competitive intern compensation
  • Mentorship from frontier voice AI researchers
  • Access to cutting-edge tools and GPU infrastructure
  • Beautiful office in Levi's Plaza, SF with rooftop views
  • Real shot at a return offer
  • Work that reaches production systems at scale
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Bland

Bland builds AI voice agents for enterprise customers, handling millions of daily calls through custom deployments and proprietary audio and language technologies. The company is hiring Forward Deployed Engineers to work directly with clients on production solutions, and ML researchers to advance multimodal LLMs and audio technologies for real-time conversational AI.

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Likely interview questions

  • Walk us through a time you reimplemented a paper result—what did you learn that wasn't in the paper itself?
  • Describe a speech or audio project where you caught something sounding wrong that metrics missed. How did you debug it?
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