Aleph Alpha Research’s mission is to deliver category-defining AI innovation that enables open, accessible, and trustworthy deployment of GenAI in industrial applications. Our organization develops foundational models and next-generation methods that make it easy and affordable for Aleph Alpha’s customers to increase productivity in finance, administration, R&D, logistics, and manufacturing processes.

WE ARE HIRING TO GROW OUR ORG IN HEIDELBERG, GERMANY, AND ARE LOOKING FOR WELL-ROUNDED, EXPERIENCED SENIOR AI SOFTWARE ENGINEERS WITH EXPERIENCE IN DEVOPS/MLOPS.

As a Senior AI Software Engineer in Aleph Alpha Research, you help the research teams take model and algorithm development to the next level. You own significant portions of the research infrastructure, including the pipelines related to data processing, our testing infrastructure, and engineering-heavy parts of our distributed training software. You also contribute your software engineering experience to research projects that have a significant influence on our ability to deliver novel category-defining AI capabilities.
As part of our Infrastructure and Platform Engineering Team, you maintain and develop cluster infrastructure, manage cloud infrastructure, build and manage DevOps and MLOps pipelines, implement SE best practices (CI/CD, monitoring, testing frameworks), and collaborate and co-develop with our Data Center Infrastructure Team as well as Product Teams on shared platform components or projects.
In our Data Engineering and Distributed Training Team, you engineer and optimize data processing pipelines, develop and maintain components of distributed training software, and build and maintain infrastructure and support for data-heavy tasks.
As part of our Research SE Teams, you work alongside Researchers and other SW Engineers on model and algorithm development, collaborate on ablation studies, Proof of Concepts (POCs), and model optimizations. You create robust, maintainable codebases that support efficient transition of new technologies and R&D artifacts from research to production, and co-own efforts that aim to make parts of our code source available to the broader research community.

BASIC QUALIFICATIONS

• 5+ years of non-internship professional experience across the full software development lifecycle, including coding standards, code reviews, source control management, build processes, testing, and operational excellence
• Demonstrated ability to solve complex and novel problems independently using state-of-the-art scientific approaches
• Proven track record in design and architecture (e.g., design patterns, reliability, scaling) of large-scale systems
• Deep expertise in at least one major programming language, and ability to independently implement complex changes to foundational systems or algorithms
• Strong communication skills, with the ability to convey complex technical concepts, anticipate scientific or engineering limitations and drive consensus across teams
• Bachelor’s degree in computer science, engineering, or a closely related field
• Ready to relocate to Heidelberg, Germany, or otherwise come to our Heidelberg office regularly, potentially weekly

PREFERRED QUALIFICATIONS

• Demonstrated skills in integrating complex systems with cross-team collaboration to enhance solution consistency and overall impact
• Proven experience designing and delivering high-performance, scalable systems into production environments
• Contributions to research outputs or top-tier publications
• Experience with systems programming and low-level languages such as Rust, focusing on performance and reliability
• Master’s degree in computer science or related fields

We do not necessarily require prior experience in machine learning for this role, but we do value your eagerness to learn. If you have prior experience in ML, we will be particularly excited about:

• Experience productizing AI research innovations into real-world applications, especially in areas such as large-scale data processing and distributed computation for foundational model training or inference.
• Familiarity with popular NLP tools and frameworks such as PyTorch or HF Transformers, with knowledge of transformer architectures.
• Ability to write clear proposals or publications, and demonstrated excellence in explaining research contributions to both technical and non-technical stakeholders.
• Proven ability to apply advanced scientific methods to novel problems, resulting in impactful outputs such as publications or projects.

YOUR RESPONSIBILITIES

Depending on your profile, you will contribute to one or more of the following areas:

• Design and (continuous) development of the research infrastructure, establish mechanisms that improve code quality, testing, and feature delivery
• Support the development, training, and maintenance of deep learning models, in collaboration with the researchers as well as the SW/HW engineers at our distributed computation centers
• Developing and optimizing lower-level code for data processing, tokenization, or research projects
• Contributing your software-engineering expertise to research projects (this could be, for example, in areas such as agent interfaces or data generation)
• Help production AI research innovations into real-world applications
• Engaging in our hiring process and otherwise mentoring engineers and researchers in terms of software development best practices
• Most of our training code is written in Python, with PyTorch being our main deep learning framework. Some of our lower-level code is written in Rust.

We do not necessarily require prior experience in machine learning for this role, but we do value your eagerness to learn. If you have prior experience in ML, we will be particularly excited about:

• Experience productizing AI research innovations into real-world applications, especially in areas such as large-scale data processing and distributed computation for foundational model training or inference.
• Familiarity with popular NLP tools and frameworks such as PyTorch or HF Transformers, with knowledge of transformer architectures.
• Ability to write clear proposals or publications, and demonstrated excellence in explaining research contributions to both technical and non-technical stakeholders.
• Proven ability to apply advanced scientific methods to novel problems, resulting in impactful outputs such as publications or projects