Skydio에서 자율주행 드론을 위한 딥러닝 인프라 엔지니어를 채용합니다. MLOps 및 컴퓨터 비전 모델의 추론 최적화 경험이 필수입니다. 고성능 추론 엔진 개발과 모델 배포 파이프라인 구축을 주도하며, 임베디드 및 클라우드 팀과 협업하여 자율 비행 기술의 성능을 극대화하는 역할을 수행합니다.
Zurich, Switzerland - Full-time
Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The Skydio team combines deep expertise in artificial intelligence, best-in-class hardware and software product development, operational excellence, and customer obsession to empower a broader, more diverse audience of drone users, from utility inspectors to first responders, soldiers in battlefield scenarios, and beyond.
Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to accelerate progress in intelligent mobile robots. If you are excited about leveraging massive amounts of structured video data to solve problems in Computer Vision (CV) such as object detection and tracking, optical flow estimation and segmentation, we would love to hear from you.
As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s Deep Learning (DL) and AI efforts. You will be working at the nexus of Skydio’s autonomy, embedded and cloud teams to deliver new capabilities and empower the deep learning team.
Develop solutions for high-performance deep learning inference for CV workloads that can deliver high throughput and low latency on different hardware platforms
Profile CV and Vision Language Models (VLMs) to analyze performance, identify bottlenecks and acceleration/optimization opportunities and improve power efficiency of deep learning inference workloads
Design and implement end to end MLOps workflows for model deployment, monitoring, and re-training
Utilize advanced Machine Learning knowledge to leverage training or runtime frameworks or model efficiency tools to improve system performance
Create new methods for improving training efficiency
Implement GPU kernels for custom architectures and optimized inference
Design and implement SDKs that allow customers/external developers to create autonomous workflows using Machine Learning (ML)
Leverage your expertise and best-practices to uphold and improve Skydio’s engineering standards
Demonstrated hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment
Strong knowledge of DL fundamentals, techniques, and state-of-the-art DL models/architectures
Strong fundamentals in CV, image processing, and video processing
Demonstrated hands-on experience building and managing ML pipelines for solving vision or vision language tasks including data preparation, model training, model deployment, and monitoring
Experience and understanding of security and compliance requirements in ML infrastructure
Experience with ML frameworks and libraries
You have demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
You are comfortable navigating and delivering within a complex codebase
Strong communication skills and the ability to collaborate effectively at all levels of technical depth
#LI-SM1
At Skydio we believe that diversity drives innovation. We have created a multidisciplinary environment that embraces the power of diverse perspectives to create elegant solutions for complex problems. We are committed to growing our network of people, programs, and resources to nurture an inclusive culture.
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristics protected by federal, state or local anti-discrimination laws.
For positions located in the United States of America, Skydio, Inc. uses E-Verify to confirm employment eligibility. To learn more about E-Verify, including your rights and responsibilities, please visit https://www.e-verify.gov/
