Alephalpha
1 day ago
Senior AI Researcher - Pre-training (f/m/d)
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Company information
- Company
- Alephalpha
- Location
- Heidelberg Germany
- Posted
- 1 day ago
Job description
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 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, optimizing 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, optimization, 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 (optimizers, schedules, initialization) and design PyTorch-based architectural improvements to maximize 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 behavior 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 behavior from smaller proxy runs.
Stability & Performance: Experience profiling distributed jobs and diagnosing training anomalies like loss spikes, numerical instability, or optimizer 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 optimization is not required, but we highly value a strong curiosity about the hardware and systems constraints that shape scale.
What we offer
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 more English Speaking Jobs in Germany on Arbeitnow
Required skills
- debugging
- optimization
- engineering
- python
- pytorch
- software engineering
- diagnostics
- evaluation
- research
- data
- architecture
- flexible working hours
- autonomy
- work-life balance
- distributed systems
- schedules
- mental health support
- llms
- stability
- ownership
- hybrid working model
- scaling
- foundation models
- empowerment
- pair programming
- predictability
- maintainable code
- convergence
- multimodal models
- open communication
- heidelberg
- complex technical problems
- ai revolution
- company pension plan
- paid vacation
- wellhub
- production code
- open-source contributions
- arbeitnow
- neurips
- quick decision-making
- nilo.health
- shared purpose
- deepspeed
- hardware constraints
- pre-training
- post-training
- gpu clusters
- icml
- iclr
- mathematical reasoning
- work in germany
- flat organizational structure
- architectural improvements
- recipe optimization
- mob programming
- research excellence
- ai researcher
- optimizers
- model lifecycle
- well-tested code
- model quality
- software engineering practices
- large-scale training
- loss spikes
- race conditions
- published research
- scaling strategy
- machine learning research
- compute performance
- top-tier publications
- training efficiency
- memory bandwidth
- rigorous experiments
- on-site collaboration
- european regulatory requirements
- megatron-lm
- torchtitan
- mixture-of-experts
- kolibri-1
- german language models
- inference-efficient architectures
- optimizing training recipes
- scaling training
- nvidia blackwell gpus
- empirical observations
- principled training decisions
- proxy experiments
- rigorous experimental design
- run reliability
- core elements
- initialization
- hyperparameter scaling laws
- scale-up methodologies
- multi-thousand-gpu behavior
- de-risk training decisions
- convergence issues
- divergence
- distributed system failures
- communication bottlenecks
- synchronization errors
- system-model co-design
- communication topologies
- pytorch training workflows
- shipped models
- mathematical foundation
- transformer training dynamics
- large distributed training jobs
- noisy results
- pre-training large models
- 7b+ parameters
- 100+ gpu clusters
- reproducible experimentation workflows
- complex model architectures
- aar/aa organization
- distributed frameworks
- predictive scaling
- hyperparameter transfer
- predicting large-scale training behavior
- smaller proxy runs
- stability & performance
- profiling distributed jobs
- training anomalies
- numerical instability
- optimizer pathologies
- advanced architectures
- sparse training
- routing trade-offs
- systems trade-offs
- track record of impact
- significant shipped technical work
- systems curiosity
- fitness & wellness offerings
- germany-wide transportation ticket
- technical equipment budget
- jobrad® bike lease
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