Member of Technical Staff - Large Scale Data Infrastructure

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

EUR 100K-230K From the posting source checked Jun 20, 2026

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff - Large Scale Data Infrastructure Freiburg (Germany), San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We're looking for infrastructure engineers who want to work at peta-to-exabyte scale. You'll build the data systems behind the largest training runs on thousands of GPUs, where fixing one bottleneck lets researchers train the next breakthrough model. What You'll Work On - Scalable data loaders for training runs across thousands of GPUs - Efficient storage and retrieval systems for petabyte-scale datasets - Multi-cloud object storage abstraction - Execute large-scale data migrations across storage systems and providers - Debug and resolve performance bottlenecks in distributed data loading Technical Focus - Python, PyTorch DataLoader internals - Object storage (e.g. S3, Azure Blob, GCS) - Parquet for metadata - Video: ffmpeg, PyAV, codec fundamentals What We're Looking For - Built and operated data pipelines at petabyte scale - Optimized data loading - Worked with petabyte-scale video and image datasets - Written

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