Machine Learning Intern
Conduct an end-to-end research project on speech and audio machine learning, training and evaluating models on large-scale telephony data and potentially advancing results toward production. Candidates should be pursuing a master’s or PhD or have equivalent research experience, with hands-on audio modeling and PyTorch expertise.
About the job
Responsibilities
- Own a focused machine learning research question end to end, from literature review through implementation, experimentation, and results.
- Design ablations to isolate the causes of model improvements.
- Present findings to the research team and defend the methodology.
- Train and evaluate models on large-scale, real-world telephony audio, including accents, noise, and production artifacts.
- Run experiments on distributed GPU infrastructure and help move promising results toward production.
- Potentially research expressive and controllable text-to-speech, neural audio codecs, speech representations, ASR robustness, real-time streaming inference, or full-duplex conversation and turn-taking.
Requirements
- Currently pursuing a master's or PhD in machine learning, computer science, electrical engineering, or a related field, or have equivalent research experience.
- Able to read and independently reimplement research papers.
- Experience with self-supervised, generative, or multimodal modeling.
- Hands-on experience with speech or audio models, including TTS, ASR, codecs, or audio representation learning.
- Strong intuition for audio quality and synthetic speech.
- Fluency in PyTorch and comfort working in a production codebase.
- Able to run experiments independently on GPU clusters.
Nice to Have
- Publications or open-source contributions in speech or language AI.
Compensation and Benefits
- Competitive intern compensation.
- Mentorship from researchers working on frontier voice AI.
- Tools and resources needed to succeed.
- Office in Levi's Plaza, San Francisco, with rooftop views.
- Potential return offer.
Skills
Machine Learning, PyTorch, Speech Recognition, Text-To-Speech, Audio Codecs, Self-Supervised Learning, Generative Modeling, Multimodal Modeling, Gpu Clusters, Distributed Computing, Streaming Inference
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