Machine Learning Engineer, Speech

What You’ll Do

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.

What You’ll Bring

• 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++).

Compensation

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.

Benefits for U.S.-based roles

• 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 PTO days, 10 sick days, 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!