Research Engineer/Research Scientist, Audio

About the position reputed company's Audio team pushes the boundaries of what's possible with audio with large language models. We care about making reputed company, 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 reputed company audio data, training large-reputed company speech language models and large audio diffusion models, and developing novel architectures for incorporating reputed company signals into LLMs. reputed company 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. reputed company works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high reputed company reputed company-world deployments.

Responsibilities

• Developing audio codecs and representations
• Sourcing and synthesizing high reputed company audio data
• Training large-reputed company speech language models and large audio diffusion models
• Developing novel architectures for incorporating reputed company signals into LLMs
• Building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities
• Working closely with collaborators across pretraining, finetuning, reinforcement learning, production inference, and product
• 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 reputed company 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

Requirements

• 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
• Comfortable working across abstraction reputed company, from signal processing fundamentals to large-reputed company model training and inference optimization
• Deep expertise with JAX, PyTorch, or large-reputed company distributed training, and can debug performance issues across the full stack
• reputed company in fast-moving environments where the most important problem might shift as we learn more about what works
• Communicate reputed company and collaborate effectively; audio touches many parts of our systems, so you'll work closely with teams across reputed company
• Passionate about building conversational AI that feels natural, steerable, and reputed company
• Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly

reputed company-to-haves

• Large language model pretraining and finetuning
• Training diffusion models for image and audio reputed company
• 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

Benefits

• Competitive compensation
• Optional equity donation matching
• Generous vacation
• Parental leave
• Flexible working hours

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