What You’ll Do
- Own research projects on audio training, low-latency inference and conversational responsiveness.
- Design and train large-scale models that natively support audio input and output.
- Investigate scaling behavior such as how data, model size, and compute affect capability and efficiency.
- Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.
- Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.
- Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.
Skills and Qualifications
Minimum qualifications:
- Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
- Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Clarity in communication, an ability to explain complex technical concepts in writing.
Preferred qualifications:
- A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
- Experience with real-time inference, streaming architectures, or optimization for low latency.
- Prior experience training or evaluating large-scale audio or multimodal models.
- Publications, releases, or open-source projects related to speech, audio, voice, or similar areas.
- Demonstrated experience in audio or speech modeling, including ASR, TTS, or self-supervised audio learning.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
Logistics
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.