Research, Audio Expertise

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

The mission of Thinking Machines is to build AI that extends human will and judgment.

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 — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• 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 $350,000 - $475,000 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.