Machine Learning Intern

Uncover; San Francisco, Northern (CA, KY); Hybrid; USD 34,000 - 55,000; Full time

Benefits offered by this job

Mentorship from researchers

Office in Levi's Plaza, SF

Return offer potential

Job summary

Bland in San Francisco invites a Machine Learning Research Intern to own a focused project across our voice stack—from speech-to-text to text-to-speech. You’ll work with our research team on problems they are pursuing, not a busywork assignment, aiming for results worth shipping or publishing.

You will train and evaluate models on real telephony data, use distributed GPU infrastructure, and collaborate with engineers to move promising results toward production.

Qualifications

• Currently pursuing a MS or PhD in ML, CS, EE, or related field, or equivalent research experience.
• Comfortable reading a paper and reimplementing it without hand-holding.
• Experience with self-supervised, generative, or multimodal modeling.
• 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.

Responsibilities

• 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.

Skills

PyTorch

Experimentation

Speech audio models

Self-supervised modeling

Audio quality intuition

Education

MS or PhD in ML/CS/EE

Tools

GPU clusters

Job description

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 AIEvery tool you need to succeed
• Every tool you need to succeed
• Beautiful office in Levi's Plaza, SF with rooftop views
• A real shot at a return offer