ML/Data Infrastructure Engineer

Flyability; Lavamünd; Hybrid; EUR 95.000 - 137.000; Vollzeit

Benefits dieser Stelle

Remote work up to 2 days/week

Ski weekend

Gym discounts

Swibeco benefits

Team events

Zusammenfassung

Flyability in Lausanne seeks an ML/Data Infrastructure Engineer to build and operate the data and ML infrastructure powering Elios, the world’s first collision-tolerant indoor drone. You will own data pipelines, labeling workflows, training orchestration, and model deployment, collaborating with Spatial AI and Cloud Platform engineers.

You will transform growing datasets into structured, versioned ML assets, enabling faster experimentation and reliable model delivery across the autonomous

Qualifikationen

• 3+ years of experience in data engineering, MLOps, ML infrastructure, or related software engineering.
• Strong Python and software engineering skills, with experience building production-grade systems.
• Hands-on experience with data storage, databases, and data processing.
• Practical experience with AWS, particularly S3 and cloud-based compute and storage.
• Experience with ML lifecycle components including versioning, tracking, training, or CI/CD.
• Experience with ML datasets, labeling, curation, and data quality.
• Knowledge of Docker, CI/CD, workflow orchestration, or infrastructure-as-code.
• Strong ownership and problem-solving skills; able to take an ambiguous problem to production.
• Proficiency in English; French is a plus.

Aufgaben

• Own data pools and datasets from ingesting raw data to curation and versioning for training and evaluation.
• Host in-house labeling tools and manage annotation workflows with internal and external partners.
• Automate and maintain ML training, evaluation, experiment tracking, and reproducibility.
• Build tooling to move models from training to deployment and monitor performance.
• Provide ML developer platform tools and documentation for Spatial AI engineers, collaborating with Cloud Platform teams.
• Monitor production data, feedback loops, and failure cases to improve ML workflows.

Kenntnisse

Python

Data engineering

AWS (S3)

CI/CD

Docker

ML lifecycle

Data storage

Versioning

English

Architecture

Tools

Docker

CI/CD tooling

Workflow orchestration

Infrastructure as code

Jobbeschreibung

Do you want to dive in the fast-growing industry of drones and get a rewarding experience in a dynamic scale-up environment?

At Flyability, we believe that robots should be sent into hazardous places and dangerous environments instead of humans. To support our belief, we created Elios, the world's first collision-tolerant flying robot that can safely enter, survey, and inspect confined spaces so that people don't have to. With more than 150 employees and 1'500 customers, Flyability is the market leader in the UAS indoor inspection industry. Joining Flyability is not just taking on 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 seeking a:

ML/Data Infrastructure Engineer (100%)

Ideal starting 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 turns data collected by our inspection drones into better AI capabilities.

You will own 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 own:

• Data pools & datasets: Own the infrastructure and workflows for our ML data pools and datasets from ingesting and organizing raw data to curating, validating, and versioning the datasets used for training and evaluation.
• Data & labeling infrastructure: Host in-house labeling tools and manage annotation workflows, ensuring seamless data flows 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 tooling and automation to move models smoothly from training and validation to reliable deployment.
• ML monitoring & feedback: Monitor training and model performance, connecting production data and failure cases back into the ML development loop.
• ML developer platform: Provide the tools, documentation, and workflows that enable Spatial AI engineers to go from data to deployable models, collaborating with the Cloud Platform Engineer.

Requirements

Your profile:

• 3+ years of experience in data engineering, MLOps, ML infrastructure, or related software engineering.
• Strong Python and software engineering skills, with experience building production-grade systems.
• Hands‑on experience with data storage, databases, and data processing, including designing data structures and efficiently querying, transforming, and managing large datasets.
• Practical experience with AWS, particularly S3 and cloud-based compute and storage.
• Experience with the ML lifecycle, including some combination of dataset/model versioning, experiment tracking, training orchestration, model registries, or ML CI/CD.
• Experience working with ML datasets, including curation, versioning, annotation, and data quality.
• Experience with tools such as Docker, CI/CD, workflow orchestration, or infrastructure-as-code.
• Strong understanding of the practical needs of ML engineers and the ability to build infrastructure that makes their work faster and more reproducible.
• Strong ownership and problem‑solving skills, with the ability to take an ambiguous problem from architecture to production.
• Proficiency in English; French is a plus.

Nice to have:

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

Benefits

Perks & Benefits You'll Love:

• Enjoy 25 vacation days per year, plus all public holidays to recharge and explore
• Additional days off are granted based on your seniority with us, up to 5 days.
• Stay secure with comprehensive accident insurance covering medical treatments and hospitalization
• Work your way with flexible schedules and the option to work remotely up to 2 days per week
• Boost your well-being with discounts on gym memberships and sports events
• Access exclusive benefits through Swibeco, our platform that offers discounts and rewards at a wide range of retailers and services.
• Connect with your team at exciting events like our ski weekend, summer barbecue, and after-work gatherings

Flyability is a Swiss company with over 10 years of experience that values independent thinking combined with a collaborative spirit. Every day, you will have the opportunity to share your ideas and contribute to solving problems. We all work together, and each voice is considered as we collaborate to achieve our goals.