Research Engineer, Generative Video
Build and scale generative video and multimodal models, optimizing training and inference for efficiency, throughput, and ultra-low latency. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and the ability to move research models into production.
About the job
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
- Bachelor’s, master’s, 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 for late work.
- Grubhub subscription.
- Health and wellness perks.
- Multiple team offsites and regular team events.
- Generous paid time off policy.
Skills
PyTorch, CUDA, Triton, Distributed Training, Fsdp, Deep Learning, Diffusion Models, Autoregressive Models, Quantization, Model Pruning, Knowledge Distillation, Gpu Optimization, Performance Profiling, Multimodal Models, Video Generation
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