PhD Autonomy Engineer Intern - Planning & Controls (Reinforcement Learning)

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified

Salary

Not verified

Was this benefit information wrong? Tell us.

Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Entry From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

PhD Autonomy Engineer Intern - Planning & Controls (Reinforcement Learning) Zurich, Switzerland, Zurich, Switzerland 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 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 geometric 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 → 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

Read the full description at jobs.ashbyhq.com. FewerJobs shows a preview and links to the original posting.