Research Engineer/Research Scientist, Audio

About the role

Anthropic’s Audio team pushes the boundaries of what's possible with audio, focusing on creating safe, steerable, and reliable systems that can understand and generate speech and audio. As a Research Engineer/Research Scientist on the Audio team, you’ll work across the full stack of audio machine learning, developing audio codecs and representations, sourcing and synthesizing high-quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into large language models.

Responsibilities

• Develop and train audio codecs and representations
• Sourcing and synthesizing high-quality audio data
• Training large-scale speech language models and large audio diffusion models
• Developing novel architectures for incorporating continuous signals into LLMs
• Collaborating with teams across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high impact real-world deployments

Requirements

• Hands-on experience with training audio models
• Comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization
• Experience with JAX, PyTorch, or large-scale distributed training
• Experience with reinforcement learning for large language models and diffusion models
• Experience with GPUs, Kubernetes, and PyTorch

Qualifications

• Master’s degree or equivalent in Computer Science, Electrical Engineering, Applied Mathematics, or a related field
• Deep expertise with JAX, PyTorch, or large-scale distributed training
• Experience with large language model pretraining and finetuning
• Experience with training diffusion models for image and audio generation
• Experience with reinforcement learning for large language models and diffusion models
• Experience with end-to-end system optimization, from performance benchmarking to kernel optimization
• Experience with GPUs, Kubernetes, PyTorch, or distributed training infrastructure

Skills

• Communication skills
• Collaboration skills
• Passion for building conversational AI that feels natural, steerable, and safe
• Interest in the societal impacts of voice AI and a desire to contribute to responsible development

Benefits

Anthropic offers a competitive annual salary range of $350,000 - $500,000 USD.

Pay

The annual compensation range for this role is listed below.

Schedule

Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Location-based hybrid policy

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research.

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