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Thinking Machines LabThinking Machines LabSan Francisco, CA

Research, Audio Expertise

Conducts research to advance audio capabilities in AI models, designing and training large-scale multimodal systems, building audio data pipelines, and publishing findings. Requires ML expertise, Python proficiency, and experience with deep learning frameworks.

350k – 475k
On-siteAI Research

About the role

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.

Skills

PyTorchTensorFlowJAXPythonMachine LearningDistributed TrainingAudio ModelingAsrTtsMultimodal Models

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