Skydio has 21 active AI-related job listings. The company's hiring is distributed across several stages, with "serving infrastructure" and "post-training" each accounting for 24% of the roles, followed by "application" and "agents" at 19% each. Engineering roles are the most frequent, representing 19 of the total positions. Skydio is primarily hiring in the United States and Switzerland. Frequent tech tags include model_serving, inference_infra, embodied_ai, synthetic_data, and fine_tuning, suggesting a focus on deploying and optimizing AI models for real-world applications. In the last 30 days, Skydio posted 1 new AI role, a decrease from the previous 30-day period.
Currently tracking 18 active AI roles, with 39 new openings in the last 4 weeks. Primary focus: Serve · Engineering. Salary range $47k–$278k (avg $195k).
Skydio currently has 28 active AI-related roles in our index. The most common open titles are: Autonomy Engineer - Deep Learning (2), Autonomy Engineer - Deep Learning Infrastructure (2), Autonomy Engineer - Deep Learning Model Acceleration (2), Autonomy Engineer - ML & DL Infrastructure (2), Autonomy Engineer Intern - Computer Vision/Deep Learning Fall 2026 (2). Most positions are in Engineering and Research.
Skydio's active AI hiring is concentrated in: agents (29%), application (21%), serving infrastructure (21%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Skydio is hiring AI talent in: United States (16 roles), Switzerland (11 roles).
Job postings at Skydio most frequently reference: model serving, inference infra, embodied ai, multimodal, vision.
In the past 30 days, Skydio has posted 4 new AI-related roles. That is a -67% change versus the prior 30 days (12 → 4).
| Title | Stage | AI score |
|---|---|---|
| Autonomy Engineer - Deep Learning Skydio is seeking an Autonomy Engineer specializing in Deep Learning to train and deploy optimized models for their autonomous flight systems. The role involves designing, implementing, and deploying computer vision and multimodal deep learning models, leveraging real-world and synthetic data, and optimizing models for embedded hardware. The focus is on real-world applications of deep learning in robotics and computer vision. | Post-trainServe | 9 |
| Autonomy Engineer - Deep Learning Skydio is seeking an Autonomy Engineer - Deep Learning to train and deploy optimized deep learning models for their autonomous flight systems. The role involves designing, implementing, and deploying computer vision and multimodal models, leveraging real-world and synthetic data, and optimizing models for embedded hardware. The ideal candidate has a strong background in deep learning, computer vision, and experience deploying models to production. |
| Post-trainServe |
| 9 |
| Senior Autonomy Engineer - Deep Learning Senior Autonomy Engineer focused on designing, implementing, and optimizing deep learning solutions for real-time object detection, tracking, segmentation, and optical flow estimation on drones. The role involves leveraging state-of-the-art methods like unsupervised learning and foundational models, curating synthetic data, and refining models for low-latency embedded hardware, with a strong emphasis on computer vision and robotics applications. | Post-trainServe | 9 |
| Autonomy Engineer Intern - Deep Learning (Computational Photography) The role focuses on designing, implementing, and deploying deep learning models for autonomous flight systems, with a specific emphasis on computational photography applications. It involves training and optimizing models for both embedded hardware and cloud deployment, using real-world and synthetic data, and developing evaluation benchmarks. | Post-trainServe | 8 |
| Autonomy Engineer Intern - Deep Learning (Computational Photography) The role focuses on designing, implementing, and deploying deep learning models for autonomous flight systems, with a specific emphasis on computational photography applications. It involves training and optimizing models for both embedded hardware and cloud deployment, using real-world and synthetic data, and developing evaluation benchmarks. | Post-trainServe | 8 |