Member of Technical Staff - Research Engineer

📋 Description

• Embed in production training and tackle hard training-system problems
• Improve performance, reliability, and numerical stability of production runs
• Profile and optimize full training steps across model, attention, kernels, data loading, and memory
• Debug distributed training failures and build profiling harnesses
• Translate architecture changes into efficient training implementations
• Collaborate with researchers to move research into production-ready code

🎯 Requirements

• Experience on large-scale training systems, ideally with researchers
• Strong PyTorch fluency; comfortable with low-level training code
• Distributed training concepts: FSDP, tensor/model/context/sequence parallelism, NCCL
• Hands-on experience improving throughput, memory, or stability
• GPU profiling with Nsight Systems/Compute, torch profiler, trace viewers, or custom telemetry
• Understanding of low-precision training and quantization tradeoffs (FP8, MXFP8, FP4)

🎁 Benefits

• Distributed team with offices in Freiburg and SF; in-person weeks and travel support
• Collaboration across researchers and engineers to advance frontier models