cantina - Machine Learning Engineer, Speech - Joint Audio-Video Modeling
Requirements
• Exceptional research/development experience with large-scale audio models (>8B parameters, >500k hours of data). • Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation. • Deep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent/tokenizer design, reconstruction and perceptual objectives, adversarial training. • Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent). • Strong software engineering skills with a proven track record of building complex systems. • Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production-quality code. • Shipped large-scale speech/audio or multimodal generative models to production. • Background in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals. • Experience with voice cloning, speech control/steerability, or expressive speech generation. • Notable publications and/or open-source contributions in speech/audio/ML. • Strongly preferred: • Experience with multimodal audio-video modeling: joint AV generation of multi-shot, multi-speaker scenes with dialogue, music, and sound design generated jointly with video, and the cross-modal alignment that keeps them in sync. • Experience with video generation: video diffusion/flow-matching transformers, video VAEs, conditioned and multi-shot generation, building data pipelines for video models. • Streaming or real-time generation, causal distillation (e.g., Self Forcing / Self Forcing++).
Responsibilities
• See research and engineering as two sides of the same coin and enjoy owning work end-to-end. • Are excited to work across modalities and collaborate closely with a video generation team rather than staying inside audio. • Are results-oriented, flexible, and willing to pick up whatever moves the needle. • Like collaborating closely with infra, data, and product to ship measurable improvements. • Enjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality. • Are eager to learn every day, and to find and solve unique large-scale problems. • Audio Representations: Design, train, and improve the audio VAEs, neural codecs, and vocoders our generative models sit on top of latent design, reconstruction and perceptual objectives, compression-vs-fidelity tradeoffs. • Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) diffusion and flow-matching transformers for large-scale audio and video generation. • Joint Audio-Video Modeling: Design the audio conditioning and cross-modal alignment inside joint AV models, audio latents alongside video latents, reference-audio and multi-speaker conditioning, multi shot generation audio/video modeling. • Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models. • Data Ownership: Define data requirements and collaborate on acquisition, curation, AV-sync and quality filtering, annotation quality, and synthetic data strategies for paired audio-video and speech corpora. • Rigorous Evaluation: Design automated objective/subjective evaluations audio fidelity and intelligibility metrics, AV-sync, listening and viewing tests, robustness & bias checks, and red-team studies. • Inference Efficiency: Drive distillation, step-count reduction, quantization, and kernel/memory optimization to meet interactive latency and cost targets. • Pipeline Delivery: Harden the training → evaluation → inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback. • GPU Scaling: Partner with infrastructure to run distributed training/inference on cloud fleets and productionize models with reliability and observability. • Project Leadership: Independently lead small research projects while collaborating on larger team initiatives, including cross-team work with video generation. • Tool Development: Develop and improve dev tooling to enhance team productivity. • Safety & Responsibility: Contribute to safety/consent guardrails, watermarking, and misuse/abuse mitigation for responsible voice and likeness technology.
Benefits
• The anticipated annual base salary range for this role is between $200,000-$220,000 (€170,000-€190,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. • Competitive salary and generous company equity • Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina • 42 days of paid time off, including: • 15 company holidays • 2 floating holidays • Generous parental leave & fertility support • 401(k) retirement savings plan • Lifestyle spending account – $500/month to use however you’d like • Complimentary lunch and snacks for in-office employees • One Medical membership, and more!
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