Software Engineer, AI Systems & Infrastructure - AI Geospatial Assistant Team

<div class="content-intro"><p><strong>Welcome to Planet. We believe in using space to help life on Earth.</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p></div><p><strong>About the Role:</strong></p> <p>Planet’s mission is to image the entire world every day, making global change visible, accessible, and actionable. We are at a critical inflection point: moving from broad AI research to a delivery-focused "productization" model. To drive this, we are building a new product group focused on launching an AI Geospatial Assistant that transforms how our customers interact with global imagery to solve high-stakes problems in forensics and daily change detection.</p> <p>Our goal is to make these complex insights accessible through an intuitive interface that requires zero user training. Operating with a zero-to-one startup mindset, this team prioritizes weekly learning velocity and customer-driven graduation criteria to move rapidly from private alpha to general availability.</p> <p>As a Software Engineer, you will help to build the backend systems that bring our AI Geospatial Assistant to life. While our research teams develop the core models, you will be responsible for the 'last mile' of delivery, architecting the high-throughput backend services, scaling our systems, and ensuring our agentic workflows are fast, reliable, and cost-effective at a global scale.</p> <p>This is a full-time, hybrid role which will require you to work from our San Francisco office 3 days per week.</p> <p><strong>Impact You'll Own:</strong></p> <ul> <li>Develop and optimize multimodal LLM applications</li> <li>Work with and support the infrastructure needed for scaling and delivering embeddings</li> <li>Architect AI Orchestration: Build and maintain the high-scale systems required for LLM orchestration and agentic workflows, ensuring low-latency responses across petabytes of imagery</li> <li>Operationalize Research: Collaborate with backend engineers to transition experimental AI models into stable, low-latency production inference endpoints</li> <li>Build AI Observability: Implement production-grade monitoring, logging, and tracing for our AI services to ensure reliability and facilitate rapid debugging of our systems</li> <li>Benchmark Performance: Define model success criteria and instrumentation to ensure the assistant consistently outperforms vanilla LLM alternatives</li> </ul> <p><strong>What You Bring:</strong></p> <ul> <li>Bachelor’s Degree in computer science or an equivalent field</li> <li>4+ years of experience building and scaling high-performance backend systems in Python, Go</li> <li>Proficiency in AWS or GCP, including experience with Infrastructure as Code (Terraform) and CI/CD pipelines for high-availability services</li> <li>Solid familiarity with LLM orchestration frameworks (LangChain, LlamaIndex) from an implementation perspe ... (truncated, view full listing at source)