Intern, Mission Optimization Engineer

Planet Labs
San Francisco, CAPosted 3 April 2026

Job Description

Welcome to Planet. We believe in using space to help life on Earth. Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one. Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world’s toughest obstacles. As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains. We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world. Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, Austria, Slovenia, and The Netherlands. Internships at Planet: Planet’s Summer 2026 internships are full-time, paid 12-week positions located in our San Francisco HQ Office. Program Dates: June 1 - August 21 June 22 - September 11 About the Role: The Mission Optimization team supports all Planet’s missions, working to maximize the productivity of the world's largest Earth Observation satellite constellation. Our team spans North America and Europe. Given the scale of our fleet, automation is at the heart of our operations. You'll develop and maintain critical software that is used to manage and operate our fleet, transforming large datasets into actionable metrics. As a Mission Optimization Engineer Intern, the main scope of your project will be fleet-wide productivity optimization. You will help identify possible inefficiencies in the automated services we use to operate our satellites, and design and implement solutions to improve the overall productivity of the fleet. We are looking for a data-driven, self-starter engineer with effective communication skills that collaborates well within a technical team and adapts quickly to changing tasks and contexts. You will also work closely with hardware and software experts across teams to provide operational insights that shape our next-generation satellites. This is a full-time, in-office position based in our San Francisco office 5 days per week. Impact You'll Own: Develop and maintain real-time, mission-critical software solutions for satellite operations Write scripts to aggregate satellite data and generate system monitoring metrics Analyze data to identify issues and inefficiencies in the full stack of services and propose solutions to improve productivity Identify and drive improvements to fleet capacity and productivity through process enhancements What You Bring: Currently enrolled in a BSc in engineering or computer science Proficiency in software development, particularly in Python or equivalent languages Data-driven mindset with experience in statistical analysis Proactive, self-starter approach to engineering and problem-solving Effective interpersonal and collaboration skills within a technical team What Makes You Stand Out: Experience with agile software development Experience with DevOps tools and practices (e.g., Git, GitLab, Docker, Jenkins, Bash, Linux, CI/CD, Sentry, Datadog) Familiarity with satellite engineering and/or the space environment and its challenges Hands-on experience operating spacecraft or remote systems Familiarity with automation and optimization frameworks Contributions to open-source software projects Application Deadline: April 13, 2026 at 11:59p PT EAR/ITAR Requirements: This position requires access to export-controlled information, and as such, employment (or hiring of ... (truncated, view full listing at source)
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