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Complex day at Zipline

Choose obstacle perception before expansion locks in

You’re the ai / ml engineer. Your team is in the room. Printed Oct 12, 2026.

Obstacle caution protects aircraft safety but leaves ready orders waiting.

The release must preserve conservative approach behavior while avoiding holds caused by incomplete 3D interpretation.

Who you’d be doing this for

“We had the order loaded, then the aircraft held long enough that the customer called us twice.”

Pia Jensen · Fulfillment Operations Manager

Runs staging and handoff at a partner location where held aircraft leave prepared orders waiting.

What is at stake

Ready orders are missing pickup windows when aircraft hold near tall obstacles despite clear-looking approaches. You have to weigh conservative safety behavior against a release that keeps deliveries moving.

Why it isn’t already fixed

Every obvious fix costs something else. That’s the part you’d have to decide.

  • conservative obstacle behavior vs delivery completion
  • field evidence vs synthetic coverage
  • model accuracy vs onboard compute headroom
  • release timing vs validation confidence

Why Zipline

Droid deliveries depend on multi-camera scene geometry reaching the mission planner within an onboard compute budget.

Written with these in mind

robotics perception engineercomputer vision ML engineerautonomy validation-minded engineer

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This is the setup. The work is inside.

Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.