Performance Engineer
Posted 15 Oct 2026
Advertised as “Senior AI R&D Engineer - Performance - Pre-training (f/m/d)”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
Our Mission
Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training like our recently released Kolibri-1 model. Our customers - in finance, manufacturing, public administration - need models that understand German, meet European regulatory requirements, and work reliably in high-stakes settings. We're building that in Heidelberg.
We are hiring a Senior Performance Engineer to grow our pre-training efficiency team. If you are excited about making models fast and scaling them to thousands of GPUs, this is the role for you!
Team Culture
At Aleph Alpha, we foster a culture built on ownership, autonomy, and empowerment. Teams and individual contributors are trusted to take responsibility for their work and drive meaningful impact. We maintain a flat organizational structure with efficient, supportive management that enables quick decision‑making, open communication, and a strong sense of shared purpose.
About the role:
You will engineer the systems required to train foundation models at scale. Your objective is to maximize hardware utilization and training throughput on our large-scale GPU clusters (thousands of NVIDIA Blackwell GPUs). You will work at the intersection of deep learning frameworks, distributed systems, and GPU microarchitecture, eliminating bottlenecks from the Python layer down to the GPU kernel.
This role is for Aleph Alpha Research GmbH.
Your responsibilities:
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End-to-End Optimization: Profile training loops using PyTorch Profiler, Nsight Systems and Nsight Compute to identify system- and kernel-level bottlenecks in order to maximize model throughput.
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Distributed Strategy and Topology: Configure and tune composite parallelism strategies (e.g. TP, DP, HSDP/FSDP, EP), optimizing load balance, minimizing critical-path bottlenecks, and managing communication-to-computation trade-offs for large-scale LLM training.
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Hardware-Aware Modeling: Partner with AI Researchers to define model architectures for hardware efficiency without compromising convergence.
Your Profile
Basic Qualifications
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Are proficient in Python and the PyTorch library.
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Have a strong engineering background in parallel and/or distributed systems with proven track record of excellence.