Senior AI R&D Engineer - Performance - Pre-training (f/m/d)

Alephalpha · Heidelberg · Hybrid

• Employment typeFull-time
• Experience levelSenior
• Salary signalSalary not listed
• Last checkedJust now
• Source valid throughNov 15, 2026

Role Overview

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: 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. Your Profile 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. Compensation and Benefits 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 Find Jobs in Germany on Arbeitnow

Why It Matches

This full-time role is listed as hybrid in Heidelberg. The source does not expose a salary range, so confirm compensation before investing in a long application. The match score reflects role clarity, work mode, location, freshness, salary visibility, and description depth—not a promise that the employer will select a candidate.

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