Member of Technical Staff Research Engineer
Frontier AI research lab building the FLUX family of multimodal visual-intelligence models and delivering them through an API, playground, and open weights.
Funding history
About Black Forest Labs
Black Forest Labs develops generative AI models for image, video, audio, and action prediction, alongside production API and enterprise deployment offerings.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will improve the performance, stability, and reliability of large-scale multimodal model training. You will profile training systems, implement GPU-level optimizations, investigate distributed-training failures, validate numerical behavior, and build benchmarking tools that connect systems improvements to model-quality outcomes.
Requirements
- Experience with large-scale training systems
- Strong PyTorch knowledge and ability to modify low-level training code
- Knowledge of distributed training, including FSDP, parallelism, activation checkpointing, NCCL, and compute-communication overlap
- Experience improving throughput, memory use, or stability in real training runs
- Experience profiling GPU workloads
- Understanding of low-precision training and quantization tradeoffs
- Ability to partner with researchers on ablations and measurements
Responsibilities
- Improve performance, reliability, and numerical stability of production training runs
- Profile full training steps across model code, kernels, data loading, communication, optimization, checkpointing, and memory
- Implement and validate GPU-level optimizations
- Advance low-precision training and assess quality tradeoffs
- Translate architecture changes into efficient training implementations
- Debug distributed-training failures and throughput regressions
- Build benchmarking and profiling harnesses
- Turn repeated training failures into improved abstractions and tools
Benefits
- Equity
- Coverage of reasonable travel costs for required in-person time
