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Anthropic · San Francisco/New York City/Seattle · Hybrid

Research Engineer/Research Scientist, Audio

1/20/2026

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

Large language model pretraining and finetuning
Training diffusion models for image and audio generation
Reinforcement learning for large language models and diffusion models
End-to-end system optimization, from performance benchmarking to kernel optimization
GPUs, Kubernetes, PyTorch, or distributed training infrastructure

Training state-of-the art neural audio codecs for 48 kHz stereo audio
Developing novel algorithms for diffusion pretraining and reinforcement learning
Scaling audio datasets to millions of hours of high quality audio
Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness
Studying training dynamics of mixed audio-text language models
Optimizing latency and inference throughput for deployed streaming audio systems