Autonomy Engineer Intern, Reinforcement Learning, 2027 (HQ)

Skydio, Inc.; San Mateo (CA); On-site; USD 65,000 - 96,000; Full time

Benefits offered by this job

Benefits plans

Stipends

Job summary

Skydio, Inc. seeks a PhD student intern to advance reinforcement learning methods for autonomous drones.

You will develop policies that enable drones to plan, navigate, and control themselves in complex environments, while ensuring safety and robustness across hardware and edge compute. You will train and validate in simulation and on real drones, collaborating with perception, mapping, and product teams to bridge research and field ops.

Qualifications

• PhD student in Robotics, ML, Controls, or related field.
• Strong fundamentals in RL, control theory, and motion planning.
• Proficient in Python and at least one of C++ or CUDA.
• Hands-on experience with robotics simulation and sim2real techniques.
• Experience training/deploying policies for navigation or locomotion on real robots.

Responsibilities

• Develop and deploy RL-based policies for drone navigation and obstacle avoidance.
• Advance RL-augmented planning and safe, robust decision-making.
• Collaborate with perception, mapping, and controls teams to ship field-ready solutions.

Skills

Reinforcement Learning

Control theory

Motion planning

Python

C++

CUDA

Robotics simulation

PyTorch/JAX/RLlib

Multi-agent RL

Tools

Isaac Lab

MuJoCo

PyBullet

TensorRT

Job description

San Mateo, California, United States- Intern

About the role

Skydio builds the world’s most advanced autonomous drones used across inspection, public safety, defense, cinematography, and more. Your research won’t languish in a paper: it will fly, shaping how pilots and operators complete real missions in complex environments. Develop and deploy reinforcement learning (and adjacent policy-learning methods) that make Skydio aircraft plan, navigate, and control themselves more intelligently, safely, reliably, and efficiently across our ecosystem: handheld apps, ground control, cloud autonomy services, and fleet workflows.

How you'll make an impact

Navigation & avoidance in the wild

Train policies that adapt online to cluttered 3D scenes (forests, bridges, urban canyons), complementing our stack for robust obstacle avoidance and dynamic goal-seeking.

RL-augmented planning

Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight with tight safety envelopes and mission constraints.

Sim to Real at scale

Build scalable datasets and training loops with Isaac Lab, domain randomization, residual learning, and safety filters; validate on real drones weekly.

Human-in-the-loop shared control

Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors for intuitive control handoffs.

Fleet & multi-agent

Explore decentralized coordination for coverage, pursuit, and collaborative mapping with minimal comms.

What makes this internship different

Real hardware cadence

Prototype in sim, then flight test on production-ready aircraft and edge compute. Your work is meant to make a difference in the real world, not just in simulation.

Safety-first learning

We pair RL with formal constraints, estimation of uncertainty, and conservative action filters for dependable deployment in human environments.

Cross-disciplinary mentorship

Collaborate with perception, mapping, controls, and product teams to close the loop from research to field ops.

What makes you a strong fit

PhD student in Robotics, Machine Learning, Controls, or related field.

Strong fundamentals in RL, control theory, and motion planning; comfort with safety/robustness concepts.

Proficient in Python (PyTorch/JAX/Ray RLlib) and at least one of C++ or CUDA.

Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet) and sim2real techniques.

Experience training/deploying policies for navigation, manipulation, or locomotion on real robots or autonomous vehicles.

Nice-to-Haves

Experience with onboard inference optimization (TensorRT, quantization, sparsity).

Familiarity with modern policy learning beyond vanilla RL: diffusion policies, IL/BC, offline RL, model-based RL.

Experience with multi-agent RL or distributed training.

Compensation

The hourly rate for this position is $47/hr for Undergrad, $53/hr for Masters, and $70/hr for PhD students*. Compensation will vary based on factors, including skill level, proficiencies, transferable knowledge, and experience. In addition to base salary, Skydio full-time employees are eligible to enroll in our benefit plans and take advantage of a variety of incentives and stipends.

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

#LI-DK1

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.

Skydio provides equal employment opportunities to applicants and employees 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/

Skydio provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.

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