Build and scale generative video and multimodal models, optimizing training and inference for low latency, efficiency, and production reliability. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and at least two years of professional industry experience.
175k – 275k/yr
On-site5+ YOEML Engineering
About the role
Responsibilities
Train and optimize large-scale video and multimodal models.
Improve efficiency across training and inference, including memory, latency, and cost.
Implement distillation, quantization, and pruning techniques to accelerate diffusion and autoregressive generation.
Build and maintain distributed training systems.
Optimize GPU utilization, parallelism, and throughput.
Develop tooling for experimentation, evaluation, and debugging.
Translate research models into robust, production-ready systems.
Monitor and improve model performance in real-world usage.
Requirements
BS, MS, or PhD in Computer Science, Machine Learning, or a related field.
2+ years of professional industry experience.
Strong experience with deep learning systems and infrastructure.
Expertise in PyTorch, CUDA, Triton, and distributed training, including FSDP.
Experience scaling and optimizing large models under low-latency inference constraints.
Strong debugging and performance-profiling skills.
Ability to move quickly from prototype to production.
Benefits
Comprehensive medical, dental, and vision plans.
401(k) with employer match.
Commuter benefits.
Catered lunch multiple days per week.
Dinner stipend when working late.
Grubhub subscription.
Health and wellness perks.
Multiple team offsites per year and monthly team events.
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