Training Infrastructure Engineer

Benefits

Parental leave

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Non-birth-parent leave

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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

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.

Schedule

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Weekend work

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About this role

Training Infrastructure Engineer Berlin, Germany, Tübingen, Hybrid Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that "unmute" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role In this role, you'll focus on the full training stack - profiling GPU behavior, debugging training pipelines, improving throughput, choosing the right parallelism strategies, and designing the infrastructure that lets us train models efficiently at scale. You'll work across cluster management, model training, efficient data pipelines for video and audio, inference and optimizing pytorch code. Your work will shape the foundation on which all of our generative models are built and iterated. Key Responsibilities - Find ideal training strategies (parallelism approaches, precision trade-offs) for a variety of model sizes and compute loads - Profile, debug, and optimize single and multi-GPU operations using tools like Nsight and stack trace viewers to understand what's actually happening at the hardware level - Analyze and improve the whole training pipeline from start to end (efficient data storage, data loading, distributed training, checkpoint/artifact saving, logging, …) - Set up scalable systems for experiment tracking, data/model versioning, experiment insights. - Design, deploy and maintain large-scale ML

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