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Autonomy Engineer - Deep Learning

Join Skydio as a Deep Learning Engineer to design and deploy advanced models for autonomous drone navigation.

Location
San Mateo, California, United States
Compensation
$170k–$277.5k/yr
Level
mid
Type
full time

Posted by employer 10 months ago

First seen on Joblaze 1 month ago

Last verified on the company career page 1 day ago

Apply at Skydio → Save job Scanned from skydio.com

Skills & Technologies

AI in the day-to-day

Leverage massive amounts of real world video and other sensor data for data mining, curation, labeling, training and evaluation.

Requirements

Education
Master's degree

Not disclosed in this posting: years of experience, work arrangement, visa sponsorship.

Benefits

401k Match Holiday Pay Equity/Stock Options Sick Leave Paid vacation time Health Insurance Relocation Assistance

Joblaze summary

In the role of Autonomy Engineer specializing in Deep Learning at Skydio, the individual will focus on designing and deploying advanced computer vision models to enhance the company's autonomous flight systems. Proficiency in Python and PyTorch, along with experience in deep learning model development, is essential for tackling complex challenges like object detection and path planning. This position is well-suited for candidates with an M.S. or Ph.D. in relevant fields and a strong background in collaborative, technical environments. Skydio fosters a diverse and innovative culture, emphasizing the importance of varied perspectives in problem-solving.

Joblaze insights

  • Listed about a month ago — first seen on Joblaze September 3, 2026. Last confirmed on Skydio's careers page October 29, 2026.
  • This exact title is also open at 1 other location at Skydio: Zurich, Switzerland.
  • Salary band is below the typical range for AI/ML roles (median ~$190,000).
  • Starts above 68% of 116 comparable mid ai/ml roles in United States that list Python we track (median $150,000 across 51 companies). See Python salary trends
  • Python appears in 49.5% of 471 comparable mid ai/ml roles in United States; Computer Vision appears in 3.2% of 471 comparable mid ai/ml roles in United States.

Quick facts

What's the salary range?
Skydio lists $170,000–$277,500 for this role.
What's the tech stack?
Joblaze extracted these technologies from the posting: C++, Computer Vision, Deep Learning, PyTorch, Python.
What seniority level is this role?
Skydio targets mid-level candidates for this position.
Is this full-time or contract?
Full-time for this Autonomy Engineer - Deep Learning role at Skydio.

From the original posting

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 aerial robots that can autonomously navigate in unknown environments and deliver operational value to users. If you are excited about real world applications of deep learning and solving difficult problems in computer vision and autonomy, we would love to hear from you.

As a Deep Learning Engineer, you will be responsible for training and deploying optimized models to our products for solving challenging problems such as optical flow estimation, stereo depth estimation, object detection, segmentation and tracking, visual place recognition, localization and mapping, few-shot learning, occupancy networks, automated path planning etc.

How you'll make an impact:

  • Design, implement, and deploy computer vision and multimodal deep learning models for Skydio’s autonomy system

  • Leverage massive amounts of real world video and other sensor data for data mining, curation, labeling, training and evaluation

  • Leverage large scale and diverse synthetic data to power deep learning algorithms

  • Leverage state-of-the-art foundation models for knowledge distillation and label efficient learning

  • Refine and optimize models for low-latency on embedded hardware

  • Develop evaluation benchmarks and metrics to quantify the performance of autonomous systems

  • Be a generalist helping out on all aspects of the software when needed

What makes you a good fit:

  • M.S. or Ph.D. in computer science, electrical engineering or related discipline

  • Demonstrated hands-on experience designing, training and deploying deep learning models

  • Ability to deliver high quality, well-architected code (Python/PyTorch and preferably, C++)

  • Leverage state-of-the-art academic papers and literature for fast iteration

  • Ability to thrive in a fast paced, collaborative and highly technical team environment

  • Comfortable navigating and delivering within a complex codebase

  • Strong communication skills

  • Obtaining FAA Part 107 certification within the first 60 days of employment is strongly encouraged for all Skydio employees and required for certain positions

Compensation: At Skydio, our compensation packages for regular, full-time employees include competitive base salaries, equity in the form of stock options, and comprehensive benefits packages. Compensation will vary based on factors, including skill level, proficiencies, transferable knowledge, and experience. Relocation assistance may also be provided for eligible roles. The annual base salary range for this position is $170,000 - $277,500*.  Fundamentally, we believe that equity is the key to long-term financial growth, and we ensure all regular, full-time employees have the opportunity to significantly benefit from the company's success. Regular, full-time employees are eligible to enroll in the Company’s group health insurance plans. Regular, full-time employees are eligible to receive the following benefits: Paid vacation time, sick leave, holiday pay and 401K savings plan. This position and all associated benefits are subject to applicable federal, state, and local laws, as well as the Company’s policies and eligibility criteria.

*Compensation for certain positions may vary based on the position's location.

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Standard company text repeated across Skydio's postings is omitted here.

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