Senior AI Researcher- Pre-training (f/m/d)

Aleph Alpha GmbH

Heidelberg

Hybrid

EUR 120,000 - 180,000

Full time

3 days ago

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Job summary

Aleph Alpha GmbH in Heidelberg is hiring a Senior AI Researcher for our Pre-training team. You’ll own architecture, optimization, stability, and scaling decisions for next-generation foundation models, working at the intersection of research and engineering.

You will translate mathematical reasoning into principled training choices, from small proxy experiments to multi-thousand-GPU runs, delivering production-ready, high-quality code and robust models.

Qualifications

Strong background in ML research and software engineering with shipped models or publications.
Hands-on experience with pre-training large models on substantial infrastructure is desirable.
Proficiency in Python and PyTorch-based training workflows.

Responsibilities

Own core elements of the training recipe (optimisers, schedules, initialization) and design PyTorch-based architectural improvements.
Develop hyperparameter scaling laws and scale-up methodologies to predict multi-thousand-GPU behaviour.
Investigate convergence issues and resolve distributed system failures like bottlenecks and synchronization errors.
Collaborate with Compute Performance, Data, Evaluation, and Post-Training teams to align model lifecycle with hardware constraints.

Skills

Python

PyTorch

ML Research

Mathematics

Transformer Training

Experiment Design

Pre-training Large Models

Software Engineering

Distributed Systems

Education

PhD/ MSc in ML/CS

Tools

Megatron-LM

DeepSpeed

torchtitan

Job description

Our Mission

Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training. Our customers — in finance, manufacturing, and 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 AI Researcher to join our Pre-training team and to advance the architecture and training of our next generation of foundation models. If you are excited about designing inference-efficient architectures, optimising training recipes that scale reliably, and training models on a large scale cluster (thousands of NVIDIA Blackwell GPUs), we would love to hear from you.

Team Culture

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 organisational structure with efficient, supportive management that enables quick decision‑making, open communication, and a strong sense of shared purpose. We collaborate closely on complex technical problems, working in pairs or using mob programming to resolve challenging issues.

About the Role

As a Senior AI Researcher in Pre-training (f/m/d), you will own the critical technical levers that determine the success of our next-generation models: architecture, optimisation, stability, and scaling.

Working at the high-leverage intersection of research and engineering, you will translate mathematical reasoning and empirical observations into principled training decisions - from small-scale proxy experiments to multi‑thousand‑GPU runs.

We are looking for an expert who can combine rigorous experimental design with high-quality production code, directly influencing model quality, run reliability, and the efficiency of the models we ship.

Your Responsibilities

Recipe & Architecture Optimization: Own core elements of the training recipe (optimisers, schedules, initialization) and design PyTorch-based architectural improvements to maximise convergence, stability, and training efficiency.
Scaling Strategy & Predictability: Develop hyperparameter scaling laws and scale‑up methodologies, using small-scale proxy experiments to reliably predict multi‑thousand‑GPU behaviour and de‑risk major training decisions.
Stability, Diagnostics & Debugging: Investigate complex convergence issues (loss spikes, divergence) and resolve hard‑to‑reproduce distributed system failures like communication bottlenecks, race conditions, and synchronization errors.
System-Model Co‑Design: Partner with Compute Performance, Data, Evaluation, and Post‑Training teams to align the model lifecycle with hardware constraints, memory bandwidth, and communication topologies.

Core Qualifications

You are proficient in Python and deeply familiar with PyTorch-based training workflows.
You have a strong track record in machine learning research and software engineering, demonstrated through shipped models, impactful open‑source contributions, or published research.
You have a strong mathematical foundation and are comfortable reasoning formally about optimisation, scaling behaviour, and training dynamics.
You deeply understand transformer training dynamics, optimisation, and the behaviour of large distributed training jobs.
You can design rigorous experiments, reason clearly from noisy results, and translate empirical observations into robust training decisions.
Hands‑on experience pre‑training large models (e‑g., 7B+ parameters) on substantial infrastructure (e‑g., 100+ GPU clusters).

You apply strong software engineering practices, including writing maintainable, well‑tested code and supporting reproducible experimentation workflows.
You are able to implement complex model architectures efficiently and reliably and to debug complex issues across model code, training dynamics, and distributed systems.
You collaborate effectively within a research and engineering team and communicate clearly about your work across Pre‑training and the broader AAR/AA organization.
You are able to work in Germany and collaborate regularly on site in Heidelberg as part of the Pre‑training team.

Preferred Qualifications

Large‑Scale Training: Hands‑on experience training LLMs or multimodal models on large GPU clusters using distributed frameworks (e.g., Megatron‑LM, DeepSpeed, torchtitan).
Predictive Scaling: Familiarity with scaling laws, hyperparameter transfer, or methods for predicting large-scale training behaviour from smaller proxy runs.
Stability & Performance: Experience profiling distributed jobs and diagnosing training anomalies like loss spikes, numerical instability, or optimiser pathologies.
Advanced Architectures: Exposure to sparse training approaches (e.g., Mixture‑of‑Experts) and an understanding of their routing and systems trade‑offs.
Track Record of Impact: Demonstrated research excellence through top‑tier publications (NeurIPS, ICML, ICLR), impactful open‑source contributions, or significant shipped technical work.
Systems Curiosity: Low‑level kernel optimisation is not required, but we highly value a strong curiosity about the hardware and systems constraints that shape scale.