Lumafield provides AI-powered industrial CT scanners and cloud-based analysis software that help manufacturers improve product quality, speed development, and automate inspection processes.
About Lumafield: Lumafield was founded in 2019 to upgrade manufacturing. We are engineers with deep experience across the product development cycle, from initial ideas to shipping hardware, across industries and specializations, who became frustrated by the cost and complexity of modern manufacturing. So we decided to upgrade it. Engineers make million-dollar decisions every day, and they need tools that give them the greatest possible insight into their products. By offering unprecedented visibility into products, as well as AI-driven tools that highlight problems and generate quantitative data, Lumafield promises to revolutionize the way complex products are created, manufactured, and used across industries. We started with industrial CT scanning, which for us was the most valuable but underutilized tool in the manufacturing toolbox, enabling us to rapidly inspect essential components non-destructively. We rebuilt the whole system, from X-ray capture, to computer vision analysis, to web-based collaboration, to the entire business model, making the most advanced manufacturing tech more accessible to every industry. Our company, like our platform, is designed for upgrades. We’re building for greater intelligence, autonomy, and speed. For deeper vision, operational excellence, and powerful insights. And then we'll upgrade it all again. Lumafield is headquartered in Cambridge, MA, and has an office in San Francisco, CA. About the role: On Lumafield’s Backend team, you work on the core of our cloud platform from REST APIs through to massively parallelized data processing pipelines operating on our X-Ray and CT image data. We leverage Python in order to move quickly but ensure that we’re getting the most out of the language through usage of powerful frameworks and parallelism concepts. Our backend team also owns their code all the way through deployment; you’ll work to build your code into Docker containers and deploy them to our different environments including cloud Kubernetes clusters and on-premises servers.