ML/Data Infrastructure Engineer

• 15 September 2026
• 100%
• Indefinite duration
• Paudex

Do you want to dive into the growing drone industry and gain enriching experience in a dynamic scale-up environment?

At Flyability, we believe that robots should be sent into dangerous places and risky environments instead of humans. To support this belief, we created Elios, the world's first collision-tolerant flying robot, capable of safely entering, inspecting, and monitoring confined spaces so that people do not have to. With more than 150 employees and 1-500 clients, Flyability is the market leader in the UAS indoor inspection industry. Joining Flyability is not just taking a new job; it is seizing the opportunity to improve the lives of millions of people and contribute to the future of robotics.

To complete our creative and dynamic team in Lausanne, we are looking for a :

ML/Data Infrastructure Engineer (100%)
Ideal start date: as soon as possible

Your role:

As a member of the Autonomy team, you will build and operate the data and ML infrastructure that transforms data collected by our inspection drones into enhanced AI capabilities.

You will be responsible for the workflows connecting data collection, preparation, and labeling, training, evaluation, and model deployment. You will work closely with our Spatial AI engineers, who develop the models, and our Cloud Platform engineer, who provides the shared cloud infrastructure.

A key part of your mission will be to transform our historical and continuously growing datasets into a structured, versioned, reliable, and accessible ML asset, enabling faster experimentation and reliable model delivery.

What you will take charge of:

• Data pools & datasets: Manage the infrastructure and workflows for our ML pools and datasets, from ingestion and organization of raw data to curation, validation, and versioning of the sets used for training and evaluation.
• Data & labeling infrastructure: Host internal labeling tools and manage annotation workflows, ensuring a smooth data flow between Spatial AI engineers and external labeling partners.
• Training infrastructure: Automate and maintain the infrastructure and workflows for ML training, evaluation, experiment tracking, and reproducibility.
• Model lifecycle: Build the tools and automation to smoothly move models from training and validation to reliable deployment.
• ML monitoring & feedback: Monitor model training and performance, linking production data and failure cases back to the ML development cycle.
• ML developer platform: Provide the tools, documentation, and workflows that allow Spatial AI engineers to move from data to deployable models, in collaboration with the Cloud Platform engineer.

Your profile:

• More than 3 years of experience in data engineering, MLOps, ML infrastructure, or related software engineering.
• Strong skills in Python and software engineering, with experience in building production-quality systems.
• Hands-on experience with data storage, databases, and data processing, including designing data structures and efficient querying, transformation, and management of large datasets.
• Hands-on experience with AWS, particularly S3 and cloud computing and storage.
• Experience with the ML lifecycle, including a combination of dataset/model versioning, experiment tracking, training orchestration, model registries, or ML CI/CD.
• Experience with ML datasets, including curation, versioning, annotation, and data quality.
• Experience with tools such as Docker, CI/CD, workflow orchestration, or infrastructure-as-code.
• Good understanding of the practical needs of ML engineers and the ability to build infrastructure that accelerates their work and makes it more reproducible.
• High autonomy and problem-solving skills, with the ability to take an ambiguous problem from architecture to production.
• Mastery of English; French is a plus.

Assets:

• Experience with MLflow, DVC, SageMaker, or similar MLOps technologies.
• Experience with annotation platforms and external labeling teams.
• Experience in deploying ML models on embedded or resource-constrained platforms.
• Experience in orchestrating pipelines for fine-tuning, evaluation, and low-latency serving of language models.

Advantages & benefits you will appreciate:

• Enjoy 25 days of leave per year, plus all public holidays to recharge and explore
• Additional days are granted based on your seniority with us, up to 5 days.
• Stay protected with comprehensive accident insurance covering medical care and hospitalization
• Work at your own pace with flexible hours and the possibility to telework up to 2 days per week
• Improve your well-being with discounts on gym memberships and sporting events
• Access exclusive benefits via Swibeco, our platform offering discounts and rewards at many merchants and services.
• Connect with your team during exciting events like our ski weekend, summer barbecue, and afterworks
• and much more! Apply now to discover everything we have to offer.

Flyability is a strong Swiss company of