Quantum Systems

(Senior) ML Platform Engineer (m/f/d)

In short

This Senior ML Platform Engineer role at Quantum Systems, a European deep-tech company building unmanned air/land/maritime systems, involves designing and operating the end-to-end ML platform—data pipelines, distributed training, experiment tracking, and model validation—for large-scale multimodal sensor data (imagery, LiDAR, odometry). The team works on autonomy and perception capabilities for UxV systems, also applying foundation/multimodal AI models to internal data workflows like semantic search and auto-labeling. It requires strong Python engineering skills, hands-on MLOps/production ML platform experience, Docker/Kubernetes, workflow orchestration tools (Argo, Kubeflow, MLflow, etc.), and solid ML fundamentals; the role includes mentoring and architectural ownership.

About the Role
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 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 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 responsible for your working hours and your work-life balance.
•Mobile Work: Option to work remotely one flexible day per week.
•Stay active: With EGYM Wellpass, you get access to thousands of fitness and sports facilities, with subsidized membership.
•Bike-Leasing: Support in staying environmentally mobile and healthy.
•Corporate Benefits: Attractive offers and discounts from well-known suppliers and brands, e.g. Adidas, Apple, Expedia.
•Employee events: Celebrate successes together.
•Lunch-Card: Daily lunch budget is sponsored.
•Company Shuttle: Convenient shuttle service from Laim in Munich to the location, with return trips at the end of the workday.
About Quantum Systems
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.

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