Engineer, Supercomputing & Distributed Systems

Responsibilities

Distributed data systems

• Design multi-stage pipelines that turn petabytes of raw data into clean, annotated datasets
• Run classification models on billions of images
• Deploy and combine LLMs to caption massive multimedia data

GPU infrastructure

• Manage distributed training and inference on 1000+ GPU Kubernetes clusters
• Solve orchestration and scaling for large-scale GPU job processing
• Scale workloads and research between clusters in multiple datacenters

Distributed training

• Profile and optimize dataloaders streaming thousands of images per second
• Profile and debug InfiniBand networking on huge training runs
• Build fault tolerance systems for large-scale pretraining
• Collaborate with researchers on evolving RL infrastructure

Applied ML pipelines

• Find clean scenes in millions of videos using distributed shot-boundary detection
• Customize and train models to filter billions of images for questions like "is this a screenshot?"
• Build the systems that bridge raw cluster capacity and research output

Requirements

• Intuition for distributed systems and great mental model of how systems interact under different conditions
• Work heavily with Python, Kubernetes, Torch, and data tools like DuckDB, Arrow

Strong candidates may have experience with:

• Python, PyArrow, DuckDB, SQL, massive relational databases, PyTorch, Pandas, NumPy
• Kubernetes
• Designing and implementing large-scale ETL systems
• Fundamental knowledge of containerization, operating systems, file-systems, and networking
• Distributed systems design
• Distributed training systems (NCCL, InfiniBand, RDMA)
• Streaming and event processing systems (Kafka, Pulsar, or similar)
• PyTorch internals, custom dataloaders, and training infrastructure