Lead Audio AI Research for Multimodal Systems

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. is seeking researchers to advance audio capabilities across pre-training, post-training, and product teams. This role blends fundamental research with practical engineering, requiring high-performance code and interpretation of technical reports.

You will collaborate with researchers, infrastructure engineers, and designers to shape AI systems that millions will use, contributing to both theory and hands-on experimentation in a highly collaborative setting.

Qualifications

• Ability to design, run, and analyze experiments with empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute.
• Proficiency in Python and at least one DL framework (PyTorch, TensorFlow or JAX).
• Bachelor’s degree or equivalent in CS/ML/Physics/Mathematics with strong theoretical grounding.
• Clarity in communicating complex concepts in writing.

Responsibilities

• Own research projects on audio training, low-latency inference, and conversational responsiveness.
• Design and train large-scale models that support audio input/output.
• Investigate scaling behavior with data, model size, and compute.
• Build and maintain audio data pipelines for training and evaluation.
• Collaborate with data and infra teams to scale audio training across distributed systems.
• Publish and share research findings, datasets, and insights with the community.

Skills

Experiment design

Empirical rigor

ML fundamentals

Python programming

Distributed training

Education

Bachelor's degree in CS/ML/Physics/Math

PhD in CS/ML/Physics/Math

Tools

PyTorch

TensorFlow

JAX

Job description

Thinking Machines Lab Inc. is seeking researchers to advance audio capabilities across pre-training, post-training, and product teams. This role blends fundamental research with practical engineering, requiring high-performance code and interpretation of technical reports.

You will collaborate with researchers, infrastructure engineers, and designers to shape AI systems that millions will use, contributing to both theory and hands-on experimentation in a highly collaborative setting.