Audio AI Research Scientist — Multimodal & Real-Time

Bison; San Francisco (CA); On-site; USD 400,000 - 500,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines in San Francisco is seeking a researcher to advance audio capabilities in multimodal AI. The role blends theory with practical engineering, requiring high-performance code and rigorous analysis.

You will work across pre-training, post-training, and product teams to push the boundaries of how AI understands and generates audio content. You will design, train, and evaluate large-scale audio models, optimize for low latency, and contribute to scalable data pipelines.

Qualifications

• Design, run, and analyze experiments with empirical rigor.
• Strong grounding in ML fundamentals and distributed compute.
• Proficiency in Python and at least one DL framework; scalable code.
• Bachelor's degree or equivalent in a relevant field; PhD preferred.
• Ability to explain complex concepts clearly in writing.

Responsibilities

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

Skills

Experiment design and analysis

Machine learning fundamentals

Clear written communication

Cross-team collaboration

Real-time inference

Education

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

PhD in CS/ML/Physics/Math or related discipline

Tools

Python

PyTorch

TensorFlow

JAX

Job description

Thinking Machines in San Francisco is seeking a researcher to advance audio capabilities in multimodal AI. The role blends theory with practical engineering, requiring high-performance code and rigorous analysis.

You will work across pre-training, post-training, and product teams to push the boundaries of how AI understands and generates audio content. You will design, train, and evaluate large-scale audio models, optimize for low latency, and contribute to scalable data pipelines.