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Autonomy Engineer - ML & DL Infrastructure

San Mateo, California, United States

Open 5 months, first seen by us 155 days ago
Pay
$170,000–277,500/year · BaseLocation-specific pay — pay source
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.
Read the full posting
Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
R&D › Autonomy — team source
Department: R&D › Autonomy
From the employer’s posting

What you’ll work on

Full posting

Skydio is the leading US drone company and the world leader in autonomous flight.

If you are excited about leveraging massive amounts of structured video data to solve open problems in object detection and tracking, optical flow estimation and segmentation, we would love to hear from you.

  • Design and implement scalable data pipelines and annotation workflows

  • Build tools for data exploration and curation across the drone fleet

  • Design pipelines for data ingestion, versioning, training, deployment and monitoring

  • Optimize and scale deep learning training workflows to speed up iteration

From the employer’s posting
Skydio is the leading US drone company and the world leader in autonomous flight. We leverage breakthrough AI to create the world's most intelligent flying machines for use by our enterprise, public safety, defense and other customers. Learning a semantic and geometric understanding of the world from best-in-class visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with deep networks, AI and ML 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 leveraging massive amounts of structured video data to solve open problems in object detection and tracking, optical flow estimation and segmentation, we would love to hear from you. As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s DL and AI training efforts. You will be working at the nexus of Skydio’s autonomy and cloud teams to deliver new capabilities and empower AI/ML solutions at Skydio.
Design and implement scalable, extensible, interactive data pipelines and annotation workflows
Build tools that leverage state-of-the-art machine learning systems for efficient data exploration and curation across the fleet of Skydio drones
Design and implement pipelines for data ingestion, versioning, model training, deployment and monitoring
Optimize and scale deep learning training workflows to improve team iteration velocity

What you’ll bring

All qualifications

Core experience

  • Hands-on data engineering with large-scale, efficient data processing pipelines
  • Hands-on experience with cloud ML platforms, containerization, ML Ops and databases
  • Experience building and managing ML pipelines from data prep through monitoring
  • Understanding of security and compliance requirements in ML infrastructure
Qualification wording
Demonstrated hands-on experience with data engineering and building large scale, performant and efficient data processing pipelines
Demonstrated hands-on experience with cloud-based ML platforms, containerization technologies, ML Ops platforms and databases
Demonstrated hands-on experience building and managing ML pipelines including data preparation, model training, model deployment and monitoring
Experience and understanding of security and compliance requirements in ML infrastructure
Apply at Skydio

Posting record

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Open 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Jun 2, 2026155 days ago
Recorded sightings
301
Last seen by us
Nov 4, 2026
Employer says posted
May 7, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 155

Up longer than most. Similar jobs usually come down between day 6 and day 69.

From 4,362 similar postings that came down. Coming down is not always a hire.

Skydio is hiring

4 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
Hiring record

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Has Skydio posted this job before?

Yes. We’ve followed one other Autonomy Engineer - ML & DL Infrastructure job there since June 2026. It is still up.

  1. ZurichStill up after 155 days, first seen Jun 2
  2. This jobUp 155 days so far, first seen Jun 2

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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 to first responders, soldiers in battlefield scenarios, and beyond.

About the Role:

Skydio is the leading US drone company and the world leader in autonomous flight. We leverage breakthrough AI to create the world's most intelligent flying machines for use by our enterprise, public safety, defense and other customers. Learning a semantic and geometric understanding of the world from best-in-class visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with deep networks, AI and ML 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 leveraging massive amounts of structured video data to solve open problems in object detection and tracking, optical flow estimation and segmentation, we would love to hear from you. As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s DL and AI training efforts. You will be working at the nexus of Skydio’s autonomy and cloud teams to deliver new capabilities and empower AI/ML solutions at Skydio.

How You’ll Make an Impact:

  • Design and implement scalable, extensible, interactive data pipelines and annotation workflows

  • Build tools that leverage state-of-the-art machine learning systems for efficient data exploration and curation across the fleet of Skydio drones

  • Design and implement pipelines for data ingestion, versioning, model training, deployment and monitoring

  • Optimize and scale deep learning training workflows to improve team iteration velocity

  • 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 data engineering and building large scale, performant and efficient data processing pipelines

  • Demonstrated hands-on experience with cloud-based ML platforms, containerization technologies, ML Ops platforms and databases

  • Experience and understanding of security and compliance requirements in ML infrastructure

  • Demonstrated hands-on experience building and managing ML pipelines including data preparation, model training, model deployment and monitoring

  • 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

  • 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.

#LI-SM1

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/

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San Mateo, California, United States

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