Black Forest Labs

4 weeks ago

Member of Technical Staff - Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 46 minutes agoStatus: Live

Job Summary

Embedded in production training to tackle performance, stability, and memory challenges for large multimodal models. You’ll optimize GPU kernels, enable low-precision training, profile training pipelines, and collaborate with researchers to translate architectural changes into efficient training implementations, owning the outcome and driving research-to-production progress.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•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
•Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk
•Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from microbenchmark improvements

Pay and Benefits

Salary: USD 180,000 - 290,000 annually

Equity and Bonus:Equity

Perks:Equity

Key Requirements

•Strong PyTorch fluency with hands-on low-level training code experience
•Experience with distributed training concepts (FSDP, tensor/model/context/sequence parallelism, NCCL)
•Hands-on experience improving training throughput, memory footprint, or stability in real training runs
•Experience profiling GPU workloads with Nsight Systems/Compute or torch profiler
•Ability to work with researchers to translate architecture changes into efficient training implementations and diagnose training faults

Experience:Generative AIMultimodalDistributed training

Skills:PyTorchCUDATritonCuTeCUTLASSNCCLNsightProfilerFP8QuantizationMemory profiling

Languages:English

Tech Stack:PyTorchCUDATritonCuTeCUTLASSNCCLNsight SystemsNsight ComputeTorch profilerFP8MXFP8FP4Quantization

Company Brief

Black Forest Labs

Develops AI-driven solutions and provides machine learning consultancy to help businesses integrate advanced models and data-driven workflows. Focuses on prototyping, model deployment, and bespoke AI services to address industry-specific problems.

Industry: Consulting

Website

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Black Forest Labs

4 weeks ago

Member of Technical Staff - Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 46 minutes agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•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
•Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk
•Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from microbenchmark improvements

Pay and Benefits

Salary: USD 180,000 - 290,000 annually

Equity and Bonus:Equity

Perks:Equity

Key Requirements

•Strong PyTorch fluency with hands-on low-level training code experience
•Experience with distributed training concepts (FSDP, tensor/model/context/sequence parallelism, NCCL)
•Hands-on experience improving training throughput, memory footprint, or stability in real training runs
•Experience profiling GPU workloads with Nsight Systems/Compute or torch profiler
•Ability to work with researchers to translate architecture changes into efficient training implementations and diagnose training faults

Experience:Generative AIMultimodalDistributed training

Skills:PyTorchCUDATritonCuTeCUTLASSNCCLNsightProfilerFP8QuantizationMemory profiling

Languages:English

Tech Stack:PyTorchCUDATritonCuTeCUTLASSNCCLNsight SystemsNsight ComputeTorch profilerFP8MXFP8FP4Quantization

Company Brief

Black Forest Labs

Industry: Consulting

Website