> Markdown version of [/jobs/ext/3784601-software-engineer](https://www.wearedevelopers.com/jobs/ext/3784601-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - **Company:** Space Exploration Technologies Corp. - **Location:** Redmond, WA, United States - **Salary:** $125,000.0 - **Contract:** Internship / Graduate position - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), C Sharp (Programming Language), C++ (Programming Language), Data Centers, Information Engineering, Extract Transform Load (ETL), Data Visualization, OSI Models, Python (Programming Language), Network Architecture, Networking Basics, Object-Oriented Software Development, Power BI, Software Deployment, Software Engineering, Software Systems, System Programming, Planning Software, Grafana, Apache Spark, Backend, Data Lakes, Semi-structured Data, Kubernetes, Information Technology, Apache Flink, Deployment Automation, Apache Kafka, Non-relational Database, Build Tools, Stream Processing, Data Pipelines, Docker, Golang - **Published:** October 16, 2026 - **Apply:** https://www.juju.com/job/1_spacex-8841324002 ## About the Role BASIC QUALIFICATIONS: + Bachelor's degree in computer science, engineering, math, physics, or a STEM discipline; OR 3+ years of professional software development experience in lieu of a degree (internship experience is applicable). + 1+ years of professional experience in software engineering, data engineering, or related fields. + Development experience in an object-oriented or systems programming language (C++, Python, Go, Java, C#, or similar). PREFERRED SKILLS AND EXPERIENCE: + Experience building capacity-planning, forecasting, or optimization systems (inventory, logistics, supply-chain, or network infrastructure). + Strong backend experience with APIs, data pipelines, and in-stream processing of structured/semi-structured data (Kafka, Spark, Flink, or similar). + Experience with relational and non-relational databases, data lakes, and ETL/ELT pipelines. + Familiarity with Kubernetes, Docker, and production deployment/monitoring practices. + Experience with data visualization and dashboarding tools (Grafana, Metabase, PowerBI, or similar). + Knowledge of networking principles, OSI model, or large-scale infrastructure. + Experience with numerical optimization, graph algorithms, simulation, or predictive modeling. + Ability to take initiative, work in ambiguous/dynamic environments, and deliver end-to-end solutions. + Excellent communication and cross-functional collaboration skills. ## Description SOFTWARE ENGINEER (STARLINK GROUND NETWORK) As a Software Engineer on the Ground Network team, you will own software systems that enable capacity planning for the next generation of Starlink's global ground infrastructure. This includes thousands of ground stations, points of presence, data centers, fiber, and associated hardware. You will build tools, models, and services that let us forecast demand, optimize deployments, quantify costs, and scale the network to serve tens of millions of users while maintaining reliability and cost efficiency. RESPONSIBILITIES: + Design, build, and maintain highly reliable software for capacity planning, forecasting, and optimization of the Starlink Ground Network. + Develop scalable data pipelines, and backend services that ingest telemetry, inventory, cost, and usage data to produce actionable capacity models and KPIs. + Create simulation, optimization, and forecasting engines that predict future ground-network needs, identify bottlenecks, and recommend deployment strategies. + Collaborate with network engineers, operators, software teams, and external partners to close the loop between planning models and real-world performance. + Own the full software lifecycle: architecture, implementation, testing, deployment, monitoring, and rapid iteration in a fast-changing environment. + Apply first-principles thinking to deep-dive into capacity, cost, and scaling problems; prototype solutions and quantify technical constraints.