About Black Forest Labs

We’re the team building the foundational layer for visual intelligence: multimodal models trained jointly on images, video, audio, and action to see, generate, and understand the world.

Our founding team pioneered latent diffusion and Stable Diffusion, the breakthroughs that made generative AI accessible to millions. With FLUX, we're pushing into generative media at scale, machines that act in the physical world, and emerging applications in computer use.

Our FLUX models top public benchmarks and run in production in some of the world's biggest platforms. We're a small, fast growing research lab competing with the largest companies in the world. From our labs in Freiburg to San Francisco, we're building what comes next.

Why This Role

Large-scale training is where research ideas become real, and where many of the hardest problems are no longer cleanly separated into “research” or “engineering.” A promising architecture only matters if we can train it stably, efficiently, and correctly across large GPU fleets.

In this role, you will be embedded in production training and help where the hardest systems and performance problems arise: attention performance, custom kernels, low-precision training, profiling, memory behavior, data movement, distributed training stability, and throughput regressions. You will work directly with researchers, but your output will often be code, measurements, kernels, debugging tools, and training-system changes that make better research possible.

We are open to a range of seniority for this role. The common thread is deep technical ownership: you should be able to make progress in ambiguous training-system problems, verify your results, and own the outcome.

What You’ll Work On

Improve the performance, reliability, and numerical stability of production training runs for large multimodal generative models

Profile full training steps across model code, attention, kernels, data loading, encoders, communication, optimizer steps, checkpointing, and memory pressure

Implement and validate GPU-level optimizations: fused kernels, attention paths, low-precision matmuls, quantization kernels, CUDA/Triton/CuTe/CUTLASS experiments, and no-compile alternatives where they make sense

Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk, and quality tradeoffs against baseline training runs

Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from changes that only look good in a microbenchmark

Debug distributed training failures: NaNs, loss spikes, silent numerical drift, memory leaks, stragglers, bad nodes, NCCL issues, and throughput cliffs

Build benchmarking and profiling harnesses that make performance claims trustworthy across hardware, shapes, sequence lengths, and training configurations

Help the training team move quickly when an urgent bottleneck appears, while turning repeated failures into better abstractions and tools

What We’re Looking For

Experience working deeply on large-scale training systems, ideally as part of a training group working closely with researchers

Strong PyTorch fluency, including comfort reading and modifying low-level training code rather than only using high-level APIs

Experience with distributed training concepts such as FSDP, tensor/model/context/sequence parallelism, activation checkpointing, NCCL, and overlapping compute and communication

Hands-on experience improving training throughput, memory footprint, or stability in real training runs

Experience profiling GPU workloads with tools like Nsight Systems, Nsight Compute, torch profiler, trace viewers, or custom telemetry

Practical GPU performance judgment: you may use modern coding agents and tools as much as you want, but you need the understanding to verify correctness, numerical behavior, and performance, and to own the result

Understanding of low-precision training and quantization tradeoffs: FP8, MXFP8, FP4/NVFP4-style formats, scaling, accumulation, numerical validation, and convergence risk