# Autonomy Engineer - Deep Learning Infrastructure

**Company:** [Skydio](https://hotfix.jobs/companies/skydio)
**Location:** Zurich, Switzerland
**Role:** ML Engineering
**Skills:** MLOps, Computer Vision, Deep Learning, Machine Learning, Gpu Kernels, Model Deployment, Model Optimization, Edge Deployment, Image Processing, Video Processing, Python, C++, CUDA, Model Monitoring, Vision-Language Models
**Posted:** 2026-05-27

> Builds and scales infrastructure for deep-learning and computer-vision systems, including optimized edge inference, MLOps pipelines, GPU kernels, and deployment SDKs. The role requires hands-on experience with ML infrastructure, inference optimization, computer vision, and end-to-end model lifecycle management.

## Job Description

## Responsibilities
- Develop high-performance deep learning inference solutions for computer vision workloads, delivering high throughput and low latency across hardware platforms.
- Profile computer vision and vision-language models to identify bottlenecks, acceleration and optimization opportunities, and improve inference power efficiency.
- Design and implement end-to-end MLOps workflows for model deployment, monitoring, and retraining.
- Apply machine learning expertise to training and runtime frameworks and model-efficiency tools to improve system performance.
- Develop methods for improving training efficiency.
- Implement GPU kernels for custom architectures and optimized inference.
- Design and implement SDKs that enable customers and external developers to create autonomous workflows using machine learning.
- Uphold and improve engineering standards.

## Requirements
- Hands-on experience with MLOps, machine-learning inference acceleration and optimization, and edge deployment.
- Strong knowledge of deep-learning fundamentals, techniques, and current deep-learning architectures.
- Strong fundamentals in computer vision, image processing, and video processing.
- Hands-on experience building and managing machine-learning pipelines for vision or vision-language tasks, including data preparation, model training, model deployment, and monitoring.
- Understanding of security and compliance requirements in machine-learning infrastructure.
- Experience with machine-learning frameworks and libraries.
- Ability to drive concepts through the software lifecycle, including architecture, development, testing, deployment, and monitoring.
- Ability to navigate and deliver within complex codebases.
- Strong communication and collaboration skills.

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