Autonomy Engineer - Deep Learning Model Acceleration

Skydio

Zürich, CH

On-site

Who this role is best for

Best suited to mid-level engineers with expertise in deep learning model acceleration and computer vision, working in autonomous flight technology.

Best fit for

Engineers with hands-on experience in MLOps and ML inference optimization for edge deployment.
— “Demonstrated hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment”
Candidates proficient in CV and video processing with strong DL fundamentals.
— “Strong fundamentals in CV, image processing, and video processing”
Developers comfortable navigating complex codebases and delivering end-to-end solutions.
— “You are comfortable navigating and delivering within a complex codebase”

Things to consider

Requires collaboration across autonomy, embedded, and cloud teams.
— “working at the nexus of Skydio’s autonomy, embedded and cloud teams”
Focus on improving power efficiency of deep learning inference workloads.
— “improve power efficiency of deep learning inference workloads”

How to stand out

Highlight specific projects where you optimized DL models for edge devices.
— “Develop solutions for high-performance deep learning inference for CV workloads”
Demonstrate experience with GPU kernels for custom architectures.
— “Implement GPU kernels for custom architectures and optimized inference”
Showcase your ability to design and manage ML pipelines for vision tasks.
— “building and managing ML pipelines for solving vision or vision language tasks”

Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · TeamLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Developed high-performance deep learning inference
Profiled CV and VLMs
Implemented MLOps workflows
Created autonomous workflows

Typical background

Deep learningComputer visionMachine learning

Skills & requirements

Required

Deep LearningComputer VisionMlopsGPU KernelsSDK DevelopmentModel DeploymentMonitoringTraining EfficiencySecurity And Compliance

Preferred

RoboticsAutonomous Systems

Stack & domain

Deep LearningComputer VisionMlopsModel DeploymentMonitoringRe-trainingGpu KernelsSdksMl FrameworksSecurity And ComplianceCi/cd PipelinesAutomation ToolsContainerized ApplicationsVersion ControlRelease ManagementCommunicationCollaborationProblem-solvingSystematic ApproachCodebase NavigationAutonomyEmbedded SystemsCloudAICvVlmsML

About the role

Original posting from Skydio via Ashby

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 https://www.skydio.com/solutions/energy-and-utilities to first responders https://www.skydio.com/solutions/public-safety, soldiers in battlefield scenarios https://www.skydio.com/solutions/national-security/tactical-isr, and beyond https://www.skydio.com/solutions.
About the role:
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 model acceleration 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.
How you’ll make an impact:
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
What makes you a good fit:
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-PG1
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/

Source: Skydio careers (Ashby)