Senior Research Engineer, Foundation Model Training Infrastructure

What you'll do

• Design and maintain large-scale distributed training systems for multi-modal foundation models.
• Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
• Implement scalable data loaders and preprocessors for videos, text, and sensor data.
• Develop robust monitoring and debugging tools for training workflows on large GPU clusters.
• Integrate cutting-edge model architectures into scalable training pipelines with research teams.
• Manage large-scale GPU clusters and HPC environments using tools like Kubernetes and SLURM.

What we're looking for

• Bachelor's degree in Computer Science, Robotics, Engineering, or a related field.
• 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.
• Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
• Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
• Strong programming skills in Python and a high-performance language such as C++.
• Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools like SLURM.
• Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field (preferred).
• Demonstrated Tech Lead experience, coordinating a team of engineers and driving projects from conception to deployment (preferred).