Research Engineer/Research Scientist, Audio

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic’s Audio team pushes the boundaries of what's possible with audio with large language models. We care about making safe, steerable, reliable systems that can understand and generate speech and audio, prioritizing not only naturalness but also steerability and robustness. As a researcher on the Audio team, you'll work across the full stack of audio ML, 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 LLMs. Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high impact real-world deployments. You may be a good fit if you: 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 Strong candidates may also have experience with: 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 Representative projects: 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 The annual compensation range for this role is listed below.  For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $500,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: 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. Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. 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