ML/Data Infrastructure Engineer

ppDo you want to dive in the fast-growing industry of drones and get a rewarding experience in a dynamic scale-up environment? /p pAt 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. /p pTo complete our creative and dynamic team in Lausanne, we are seeking a: /p h3ML/Data Infrastructure Engineer (100%) /h3 pIdeal starting date: as soon as possible /p h3Your role: /h3 pAs 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. /p pYou 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. /p pA 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. /p h3What you will own: /h3 ul liData 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. /li liData labeling infrastructure: Host in-house labeling tools and manage annotation workflows, ensuring seamless data flows between Spatial AI engineers and external labeling partners. /li liTraining infrastructure: Automate and maintain the infrastructure and workflows for ML training, evaluation, experiment tracking, and reproducibility. /li liModel lifecycle: Build the tooling and automation to move models smoothly from training and validation to reliable deployment. /li liML monitoring feedback: Monitor training and model performance, connecting production data and failure cases back into the ML development loop. /li liML 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. /li /ul h3Your profile: /h3 ul li3+ years of experience in data engineering, MLOps, ML infrastructure, or related software engineering. /li liStrong Python and software engineering skills, with experience building production-grade systems. /li liHands‑on experience with data storage, databases, and data processing, including designing data structures and efficiently querying, transforming, and managing large datasets. /li liPractical experience with AWS, particularly S3 and cloud-based compute and storage. /li liExperience with the ML lifecycle, including some combination of dataset/model versioning, experiment tracking, training orchestration, model registries, or ML CI/CD. /li liExperience working with ML datasets, including curation, versioning, annotation, and data quality. /li liExperience with tools such as Docker, CI/CD, workflow orchestration, or infrastructure-as-code. /li liStrong understanding of the practical needs of ML engineers and the ability to build infrastructure that makes their work faster and more reproducible. /li liStrong ownership and problem‑solving skills, with the ability to take an ambiguous problem from architecture to production. /li liProficiency in English; French is a plus. /li /ul h3Nice to have: /h3 ul liExperience with MLflow, DVC, SageMaker, or similar MLOps technologies. /li liExperience with annotation platforms and external labeling teams. /li liExperience deploying ML models to embedded or resource‑constrained platforms. /li liExperience orchestrating pipelines for fine‑tuning, evaluation, and low‑latency serving of Language Models. /li /ul h3Perks Benefits You'll Love: /h3 ul liEnjoy b25 vacation days /b per year, plus all public holidays to recharge and explore /li liAdditional days off are granted based on your seniority with us, bup to 5 days. /b /li liStay secure with bcomprehensive accident insurance /b covering medical treatments and hospitalization /li liWork your way with bflexible schedules /b and the option to work remotely up to 2 days per week /li liBoost your well-being with bdiscounts on gym memberships and sports events /b /li liAccessb exclusive benefits /b through bSwibeco, /b our platform that offers discounts and rewards at a wide range of retailers and services. /li liConnect with your team at exciting events like our bsk