Staff Controls Engineer: Autonomous Flight Systems & Analytics

Namely; South San Francisco (CA); On-site; USD 200,000 - 280,000; Full time

Benefits offered by this job

Equity compensation

Performance bonuses

Medical, dental and vision insurance

Paid time off

Job summary

Zipline in South San Francisco, CA, is seeking a Staff Controls Engineer to own system‑performance analysis for vehicle designs and turn flight and fleet data into better models and decisions.

You will collaborate with controls, flight test, hardware, manufacturing, and customer‑facing teams to drive improvements, build reusable tools, and translate insights into engineering priorities for a high‑impact autonomous logistics network.

Qualifications

• Experience with high‑fidelity simulations to evaluate performance.
• Ability to lead ambiguous technical problems from framing to execution.
• Strong communication to explain findings to engineering and ops partners.

Responsibilities

• Own dynamic models of complex vehicle systems within Zipline’s six-degree-of-freedom simulation.
• Build controls algorithms and oversee development, testing, analysis and deployment.
• Define analysis approaches linking design choices to performance in varied conditions.
• Evaluate vehicle designs through simulation, flight-test data, and fleet data.
• Improve fidelity by reconciling model predictions with observed flight behavior.
• Lead recommendations for controls, vehicle, and system design with cross‑functional teams.
• Build reusable analysis tools for performance questions and tradeoffs.
• Translate insights into engineering priorities for the logistics network.

Skills

Controls expertise

Flight dynamics

System identification

Modeling complex systems

Job description

Zipline in South San Francisco, CA, is seeking a Staff Controls Engineer to own system‑performance analysis for vehicle designs and turn flight and fleet data into better models and decisions.

You will collaborate with controls, flight test, hardware, manufacturing, and customer‑facing teams to drive improvements, build reusable tools, and translate insights into engineering priorities for a high‑impact autonomous logistics network.