Research Engineer in Audio at Anthropic working on building conversational AI systems. Developing audio codecs, sourcing audio data, and training language models for audio understanding.
Responsibilities
Work across the full stack of audio ML, developing audio codecs and representations
Source and synthesize high quality audio data
Train large-scale speech language models and large audio diffusion models
Develop novel architectures for incorporating continuous signals into LLMs
Build advanced steerable systems spanning end-to-end conversational systems
Collaborate with many teams across pretraining, finetuning, reinforcement learning, production inference, and product
Requirements
Have hands-on experience with training audio models, whether that's conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, or generative audio models
Genuinely enjoy both research and engineering work, and you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
Are comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization
Have deep expertise with JAX, PyTorch, or large-scale distributed training, and can debug performance issues across the full stack
Thrive in fast-moving environments where the most important problem might shift as we learn more about what works
Communicate clearly and collaborate effectively; audio touches many parts of our systems, so you'll work closely with teams across the company
Are passionate about building conversational AI that feels natural, steerable, and safe
Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly.
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