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Machine Learning Intern

Bland · On-site

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Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from Bland's Ashby job board and last checked it on September 12, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: On-site
Salary: Not listed by source
Location: San Francisco
Company website: better.com

Hiring context

How this role compares at Bland

Bland has 20 live roles in MeritLog’s catalog across 7 job families, and 6 of them are in data & analytics. 13 of those listings publish a pay range, a disclosure rate of 65%.

Bland concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

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.
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.
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
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.
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.
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.

Parsed by MeritLog from the employer’s own posting. The full description follows below.

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

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