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

Aleph Alpha

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, 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 (We encourage you to apply even if you don't check every box!) * 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

Aleph Alpha

OUR MISSION Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training. 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're growing our pre-training team and hiring someone to passionately work on data: defining what goes into our models, building the systems that source and prepare it, and ensuring our training team has the highest-quality data to push model capabilities forward. 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 As a Senior AI R&D Engineer in Pre-training Data, you will work across the full stack of data preparation - from sourcing and acquisition to processing, filtering, and mixture design. Some weeks you'll be deep in data quality analysis, understanding what makes a corpus valuable and how its composition affects downstream performance on public and bespoke evaluation tasks. Other weeks you'll be optimising large-scale processing pipelines or building tooling that gives the team visibility into what our models are actually training on. And some weeks you'll be reading the latest research on pre-training data methods, translating findings into experiments you can run against our stack. We approach data work in an evidence-based way. Decisions about filtering strategies, data mixtures, and quality thresholds are backed by ablations - you'll design and run targeted experiments to validate that your data choices actually improve model outcomes. We are looking for someone that combines significant research experience (in industry or academia) with high engineering competence. Your work sits at high leverage: the data you source, curate and synthesize directly determines what our models learn, how well they perform, and where they fall short. You'll have direct influence on the models we ship. YOUR RESPONSIBILITIES * Co-Own data pipelines end-to-end: Design, build, and maintain the infrastructure that sources, processes, deduplicates, filters, and prepares pre-training corpora at scale. Own the conversion from curated corpora to training-ready streaming formats. * Curate and compose data mixtures: Define and iterate on the data blends used for pre-training - balancing domains, languages, quality tiers, and licensing requirements to maximise model capability. * Build data quality tooling: Develop classifiers, heuristics, and analysis frameworks that measure and enforce data quality across terabyte-scale corpora. Monitor pipeline health and data quality metrics at scale. * Close data gaps: Work with evaluation and post-training teams to identify where model weaknesses trace back to data coverage, then source or generate the data needed to address them. * Collaborate with post-training: Partner closely with the post-training team to ensure pre-training data decisions support downstream fine-tuning, alignment, and deployment goals - data choices upstream shape what's possible downstream. * Co-Own German-language data: Ensure deep, high-quality coverage of German-language corpora - this is core to our value proposition, not an afterthought. * Establish data-to-performance signal: Design and run ablation studies to validate data choices - measuring how changes in composition, filtering, or sourcing affect pre-training evaluation metrics and downstream capabilities. * Take data transparency seriously: Maintain data lineage and provenance so the team knows exactly what went into each training run. YOUR PROFILE BASIC QUALIFICATIONS * Track record of shipping impactful technical work - whether that's research, infrastructure, or both. * Strong Python skills and comfort with data engineering and ML infrastructure, including experience with deep learning frameworks, workflow orchestration, object storage, columnar data formats, and distributed processing. * Ability to reason about what a dataset contributes to model training and whether it matters - not just process data, but understand it. * Ownership mentality: you see problems through from diagnosis to solution to deployment. * Willingness to relocate to Heidelberg or travel at least fortnightly. PREFERRED QUALIFICATIONS * Experience with large-scale data processing for ML, including corpus sourcing, curation, cleaning, deduplication, and filtering. * Familiarity with data quality methods: classifier-based filtering, heuristic scoring, perplexity-based selection, and decontamination. * Understanding of foundation model training - how data composition, scale, and mixing ratios affect capabilities. * Experience with web-scale data sourcing and crawl processing (e.g., Common Crawl, WARC pipelines). * Rust proficiency (parts of our data pipeline are performance-critical). * Infrastructure knowledge - experience with Kubernetes, container orchestration, or cloud-native ML infrastructure. * PhD in machine learning, NLP, data engineering, or a related field (valued but not required - we care about what you can do). * Bonus, but not required: German language proficiency can be helpful for curating and assessing German-language data. 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 *

Aleph Alpha

OUR MISSION Aleph Alpha is one of the few companies in Europe with end-to-end in-house model development including pre- and post-training. We’re building models that have general-purpose capabilities, but also specifically excel at addressing the needs of our customers. We're growing our post-training team in Heidelberg (or hybrid in Germany) and are looking for an AI Researcher who combines a deep theoretical understanding of reinforcement learning methods with a desire to improve on the state of the art and improve model capabilities in large-scale training. 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 As a Senior AI Researcher for reinforcement learning you will shape and improve the underlying RL methodology, maintain a high-quality training code-base, and conduct large-scale experiments to hill-climb our performance benchmarks. This role is for you if you both have a strong theoretical background on RL and the engineering drive to bring these methods into production and improve on the methods as part of the reinforcement learning team. In your day-to-day you will conduct large-scale reinforcement learning experiments, derive hypotheses from the results, and iterate on both the implementation and methodology based on the observations. Together with a collaborative team, you will have direct impact on the models that we ship to our customers. This role is for Aleph Alpha Research GmbH. YOUR RESPONSIBILITIES * Hill-climb in large-scale training: Conduct large-scale LLM training runs, analyze evaluation scores in depth, propose hypotheses for improvement and directly implement them in order to maximize performance on our benchmarks. * Theoretical innovation: Stay at the bleeding edge of RL research. You will identify, implement, and iterate on novel approaches to multi-turn reinforcement learning. * Scale our training infrastructure: Identify bottlenecks in our training setup and optimize our RL training loops for large-scale training. * Cross-functional collaboration: Partner with our other post-training teams to turn raw feedback into actionable training signals, ensuring that our RL iterations lead to measurable improvements in downstream performance. YOUR PROFILE Basic Qualifications * A deep understanding of Reinforcement Learning theory and how it relates to modern RL methods. * Experience with multi-node LLM training (ideally using RL). You understand how to scale multi-node RL trainings and can reason about and implement distributed algorithms. * Familiarity with statistical methods for evaluation and experiment design. * Ability to reason about what an evaluation/environment measures and whether it matters - not just run benchmarks, but understand them. * Strong Python skills and comfort with ML tooling (especially torch distributed) * Willingness to relocate to Heidelberg or travel regularly (potentially weekly). Preferred Qualifications * PhD in reinforcement learning or equivalent research experience. * A history of contributions to top-tier venues (NeurIPS, ICML, ICLR, etc.) specifically regarding RL. * Experience evaluating LLM models and crafting environments for training. 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