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:
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.
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.
Hardware-Aware Modeling: Partner with AI Researchers to define model architectures for hardware efficiency without compromising convergence.
Basic Qualifications
Are proficient in Python and the PyTorch library.
Have a strong engineering background in parallel and/or distributed systems with proven track record of excellence.
Have hands-on experience with modern machine learning techniques (especially large language models and their life cycle).
Deeply understand the CUDA programming model.
Have experience in distributed programming with APIs like NCCL or MPI.
Have experience analysing profiling traces with tools such as PyTorch Profiler and Nvidia Nsight.
Please note this role requires regular on-site collaboration in Heidelberg as a member of the Training Efficiency Team.
Preferred Qualifications
Contributions to modern distributed training frameworks (e.g., TorchTitan, Megatron-LM, DeepSpeed).
Familiarity with low-precision training formats (MXFP4, MXFP8) and their impact on numerical stability and throughput.
A deep understanding of NCCL communication primitives, NVSHMEM or CUDA IPC and their performance.
A proven track record of implementing and optimising modern transformer-based model training.
A proven track record working on the NVIDIA Blackwell architecture.
Become part of an AI revolution!
30 days of paid vacation
Access to a variety of fitness & wellness offerings via Wellhub
Mental health support through nilo.health
Substantially subsidized company pension plan for your future security
Subsidized Germany-wide transportation ticket
Budget for additional technical equipment
Flexible working hours for better work-life balance and hybrid working model
JobRad® Bike Lease
Skills Required
- Proficiency in Python
- Proficiency with PyTorch
- Strong engineering background in parallel or distributed systems
- Proven track record of engineering excellence
- Hands-on experience with modern machine learning techniques, especially large language models and their life cycle
- Deep understanding of the CUDA programming model
- Experience with distributed programming APIs such as NCCL or MPI
- Experience analyzing profiling traces using PyTorch Profiler and NVIDIA Nsight
- Regular on-site collaboration in Heidelberg
- Contributions to distributed training frameworks such as TorchTitan, Megatron-LM, or DeepSpeed
- Familiarity with low-precision training formats such as MXFP4 and MXFP8
- Deep understanding of NCCL communication primitives, NVSHMEM, or CUDA IPC performance
- Proven experience implementing and optimizing transformer-based model training
- Proven experience working on NVIDIA Blackwell architecture
What We Do
We are an AI research and application company that researches, develops and operationalises large-scale AI models for language, image data and strategy, thereby contributing to securing Europe's digital sovereignty
