Research, Audio Expertise
Conducts research to advance audio capabilities in AI models, designing and training large-scale multimodal systems, building audio data pipelines, and publishing findings. Requires ML expertise, Python proficiency, and experience with deep learning frameworks.
About the job
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
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Skills
PyTorch, TensorFlow, JAX, Python, Machine Learning, Distributed Training, Audio Modeling, Asr, Tts, Multimodal Models
Similar jobs
AI Research jobs
Anthropic
San Francisco, CA
Research Engineer building large-scale AI capability evaluations, telemetry, data pipelines, and analysis tools for Anthropic’s Takeoff Intel team. The role requires hands-on large language model experimentation, rapid prototyping, data expertise, and strong research collaboration.
Thinking Machines Lab
San Francisco, CA
Research role focused on improving agentic coding capabilities through reinforcement-learning training, synthetic data, coding environments, reward design, and evaluations. Requires strong Python engineering, scalable distributed-training experience, and a bachelor’s degree or equivalent; research experience and a PhD are preferred.
Thinking Machines Lab
San Francisco, CA
Conduct AI safety research across data curation, post-training, evaluations, synthetic data, and red-teaming to improve model reliability on harmful and dual-use requests. The role requires AI safety experience, Python, deep learning frameworks, and scalable technical research skills.
OpenAI
San Francisco, CA
Researcher or engineer focused on designing, evaluating, and productionizing oversight systems and safety mitigations for autonomous AI agents. The role requires strong systems or security reasoning, threat-modeling ability, and experience building practical evaluations and controls.
OpenAI
San Francisco, CA
Researcher focused on training and evaluating frontier AI agents, mining incidents, and building scalable safety measurement systems. The role requires strong research or ML engineering execution, quantitative judgment, and the ability to own ambiguous projects end to end.