Research, Audio Expertise

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

About Thinking Machines

The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.

About the Role

Thinking Machines builds multimodal-first. For us, there is no separate multimodal work. It's at the core of everything we do, from the scientific goals we're setting to the infrastructure we're building. We're looking for researchers to advance the frontier of audio capabilities. You'll explore how audio models enable more natural and efficient communication/collaboration, preserving more information and capturing user intent.

This is a highly collaborative role. You'll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers. This is an opportunity to shape the fundamental capabilities of AI systems that millions of people will use.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It's an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

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

• Location: This role is based in San Francisco, California.

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $400,000-$500,000 USD.

• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.