Autonomy Engineer - Deep Learning Infrastructure

Skydio
Zurich
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Communication","Collaboration","Problem-solving"]

Build and scale the infrastructure behind Skydio’s deep learning and AI efforts for real-time computer vision on mobile robots. Develop low-latency, high-throughput inference across hardware platforms, profile and optimize vision and vision-language models for performance and power efficiency, and design end-to-end MLOps workflows for deployment, monitoring, and re-training. Create GPU kernel optimizations and SDKs that enable autonomous workflows for external developers.

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FursaFursa
Skydio
Skydio
3 months ago

Autonomy Engineer - Deep Learning Infrastructure

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live
Reposted: similar role first listed 8 months ago

Job Summary

Build and scale the infrastructure behind Skydio’s deep learning and AI efforts for real-time computer vision on mobile robots. Develop low-latency, high-throughput inference across hardware platforms, profile and optimize vision and vision-language models for performance and power efficiency, and design end-to-end MLOps workflows for deployment, monitoring, and re-training. Create GPU kernel optimizations and SDKs that enable autonomous workflows for external developers.
Location: Zurich
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Develop high-performance deep learning inference for computer vision workloads with high throughput and low latency across hardware platforms
  • •Profile computer vision and vision language models to identify bottlenecks and optimization opportunities and improve power efficiency
  • •Design and implement end-to-end MLOps workflows for model deployment, monitoring, and re-training
  • •Leverage training/runtime frameworks or model efficiency tools to improve system performance
  • •Implement GPU kernels for custom architectures and optimized inference, and build SDKs for autonomous ML workflows

Key Requirements

  • •Demonstrated hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment
  • •Strong knowledge of deep learning fundamentals, techniques, and state-of-the-art model architectures
  • •Strong fundamentals in computer vision, image processing, and video processing
  • •Experience building and managing ML pipelines for vision/vision-language tasks (data preparation, training, deployment, monitoring)
  • •Experience and understanding of security and compliance requirements in ML infrastructure
Skills:CommunicationCollaborationProblem-solving
Tech Stack:MLOpsMachine LearningComputer VisionVision Language ModelsGPU kernelsEdge deploymentML pipelines

Company Brief

Skydio
Designs and manufactures autonomous consumer and enterprise drones using AI-powered vision and navigation systems for inspection, mapping, public safety, and industrial applications. Focuses on rugged, fully autonomous flight and end-to-end drone solutions.
Industry: Drones & Aerial Logistics
Company Size: Large (251 to 1,000 employees)
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series D
Headquarters: San Mateo, United States
Founded: 2014
WebsiteLinkedIn