• Design and implement end-to-end systems for generating synthetic audio (speech, acoustics, sound) and/or video for agents interaction
• Lead complex generative AI projects from research prototype through large-scale production deployment
• Establish best practices for model evaluation, perceptual quality assessment, and monitoring of generative outputs
• Drive technical decisions and architecture for multimodal (audio/video) generation systems, balancing quality, latency, and scalability
• Identify high-impact opportunities where advances in generative modeling can meaningfully improve agents experiences
• Collaborate closely with research, product, design, and infrastructure teams to translate cutting-edge techniques into shipped features
• Mentor and provide technical guidance to engineers across the team, raising the bar for ML engineering practices
• Stay current with emerging techniques in generative modeling (e.g., diffusion, autoregressive, end-to-end architectures) and evaluate their applicability to our systems

• Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience
• Deep hands-on experience with generative audio and/or video architectures (e.g., diffusion models, autoregressive models, GANs, VAEs, end-to-end neural synthesis)
• Demonstrated ability to lead complex, ambiguous projects from research through production, and to make sound technical tradeoffs under real-world constraints (quality, latency, compute)
• Experience evaluating generative model outputs, including both objective metrics and perceptual/subjective quality assessment

• Proven experience building and shipping machine learning systems in production, with significant focus on generative modeling
• Excellent collaboration and communication skills, with a track record of working across research, engineering, and product teams
• Strong software engineering skills, with experience designing scalable ML systems and pipelines (e.g., Python, PyTorch/TensorFlow, distributed training infrastructure)
• Experience with speech synthesis (TTS), voice conversion, audio acoustics/background modeling, or conversational AI systems
• Experience with generative video/animation techniques (e.g., facial animation, lip-sync, avatar rendering, video diffusion)
• Publications in generative modeling, speech, audio, or computer vision at top-tier venues (e.g., NeurIPS, ICML, ICASSP, CVPR, Interspeech)
• Experience deploying real-time or low-latency generative models at scale
• Familiarity with multimodal modeling (joint audio-visual generation, cross-modal conditioning)
• Prior experience mentoring engineers or leading technical direction for a team

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