Full description
As a Senior Machine Learning Platform Engineer (m/f/d), you will design and build the platform that turns large-scale UxV and sensor data into validated, reproducible machine learning models.
You will own and improve the end-to-end ML lifecycle—from data processing, selection, and annotation through distributed model training, experiment tracking and validation. Your work will enable ML engineers to develop autonomy and perception capabilities efficiently, reliably, and at scale.
Beyond conventional platform engineering, you will also apply modern AI models to our internal data workflows. This may include semantic dataset search, automated metadata generation, data curation, and auto-labeling using foundation and multimodal models.
By creating the foundations on which our AI teams build and validate models, you will directly contribute to Quantum Systems’ next generation of autonomous UxV solutions.
What is your Day to Day Mission:
Design, implement, and operate scalable ML and data pipelines covering ingestion, dataset creation, training, and model validation.
Build a reliable ML platform for reproducible experimentation and training across local, on-premises, and cloud GPU infrastructure.
Develop efficient systems for large-scale multimodal datasets, including imagery, LiDAR, odometry, ground truth, and simulation data.
Establish dataset and model versioning, metadata management, quality checks, and traceability from raw sensor data to validated model artifacts.
Build and optimize distributed training workflows, job scheduling, GPU resource allocation, caching, and high-throughput data access.
Develop AI-powered tools for internal data engineering, including semantic data search, metadata enrichment, automatic annotation, and quality assessment using foundation or multimodal models.
Improve platform reliability, observability, security, cost efficiency, and developer experience.
Collaborate with ML, data, and engineering teams to translate their technical needs into scalable platform capabilities.
Mentor engineers, contribute to architectural decisions, and establish engineering standards for production-grade ML systems.
What you bring to the team:
You hold a degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline.
You have strong software engineering skills in Python and experience building reliable, maintainable software and automated workflows for data-intensive machine learning systems.
You have several years of hands-on experience building production ML platforms, MLOps systems, or large-scale data and training pipelines.
Solid knowledge of machine learning fundamentals and hands-on experience training and evaluating ML models.
You have a deep understanding of the ML lifecycle, including dataset preparation, distributed training, experiment management, model validation, and reproducibility.
You have experience with cloud platforms and containerized infrastructure using Docker and Kubernetes.
You have worked with workflow orchestration, experiment tracking, and model or artifact management technologies such as Argo Workflows, Kubeflow, Metaflow, Airflow, MLflow, or comparable solutions.
You have practical experience with large datasets, object and shared storage, data formats, partitioning or sharding strategies, and high-throughput data loading.
You are familiar with CI/CD, infrastructure as code, monitoring, logging, access control, and operating reliable production systems.
You communicate confidently in English and collaborate effectively across research, product, and infrastructure teams.
You are highly self-motivated and take technical ownership from architecture through operation.
Why Quantum-Systems:
We believe in the power of combined efforts: straightforward tech expertise paired with a customer-centric focus.
We are industry pioneers who are ambitious, bold, and visionary.
We push limits, think outside-the-box and strive for technological excellence to shape the future of aerial data.
We promise to be your runway for individual and professional growth.
Our benefits:
Company pension scheme: We support you so that you can already make provisions for later.
Flexible working hours: With trust-based working hours, you are not only responsible for your working hours, but also for your work-life balance.
Mobile Work: If things get a little busy in the office, you have the option to work remotely one flexible day per week to create the right balance.
Stay active: With EGYM Wellpass, you get access to thousands of fitness and sports facilities — and of course, we subsidize the membership.
Bike-Leasing: We support you in staying environmentally mobile and healthy.
Corporate Benefits: Your opportunity for attractive offers and discounts from well-known suppliers and brands, e.g. Adidas, Apple, Expedia.
Employee events: We not only want to grow together, but also celebrate our successes together.
Lunch-Card: Be powerful with delicious energy, daily lunch budget is sponsored.
Company Shuttle: Enjoy our convenient shuttle service that picks you up from Laim in Munich and brings you to our location, with return trips at the end of the workday.
About us:
Quantum Systems is a leading European deep-tech company for unmanned systems across air, land and maritime domains. We develop and scale integrated hardware, software and AI-powered solutions for mission-critical operations. By combining autonomous platforms with intelligent software and AI, Quantum Systems enables customers to operate, understand and act across domains.
Our commitment:
We are an open-minded company that not only values diversity, but actively promotes it. Regardless of gender, age, ethnic origin, religion, sexual orientation or disability, we firmly believe that the diversity of our employees is an essential part of our success.
At our company, every voice is heard and every perspective is valued. We believe that our differences enrich us and help us to find creative solutions and generate innovative ideas. We pride ourselves on creating an inclusive work environment where all employees can reach their full potential.
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