Research, Audio Expertise

Thinking Machines Lab•San Francisco, CA

3d•$350,000 - $475,000•Onsite

About The Position

Thinking Machines is seeking researchers to advance the frontier of audio capabilities. The role focuses on exploring how audio models can enable more natural and efficient communication and collaboration, preserving information and capturing user intent. This is a highly collaborative position working closely with researchers, infrastructure engineers, and designers across pre-training, post-training, and product teams. The role blends fundamental research and practical engineering, requiring candidates to write high-performance code and read technical reports. This is an 'evergreen role' kept open on an ongoing basis to express interest in this research area, with applications continuously reviewed for current and future opportunities.

Requirements

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.

Nice To Haves

A strong grasp of probability, statistics, and ML fundamentals. Ability to 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.

Responsibilities

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.