Satellite and drone imagery access is on the rise, and traditional image processing methods are struggling to keep up. We’ve never had more data, and yet it’s never harder than ever to gain meaningful insights.
Our scalable AI platform enables custom model training on global features, providing real-time, on-demand geospatial insights with impressive speed and accuracy. The application turns months of manual work into mere minutes, and with much better results. We work with customers from various domains, from intelligence and defence, local and federal governments, to small and large enterprise enterprises, which requires us to have a lot of flexibility on how we deploy and maintain our services.
We kicked off in 2020 and have secured $35 million in series A funding from a lineup of top US and European investors, among which Microsoft M12, Point72 Ventures, Maxar, In-Q-Tel, SAFRAN, and ISAI/Capgemini.
We're searching for a Software Engineer to join our ML Training & Inference team, where you'll build the orchestration layer that powers our geospatial AI platform. You'll work at the intersection of distributed systems and machine learning, enabling customers to train custom detection models and run inference at scale on imagery spanning continents.
What you'll do
Build and optimize ML orchestration pipelines that coordinate model training and inference across distributed worker poolsDesign resilient, high-throughput services that process terabytes of geospatial imagery through GPU-accelerated inferenceDevelop the APIs and abstractions that allow customers to chain, filter, and compose AI models for complex detection workflowsCollaborate with ML Researchers to put new models in productionTackle memory optimization, GPU autoscaling, and resource scheduling challenges unique to large-scale imagery processing
YOUR PROFILE
Strong practical knowledge of Python with experience building production systemsExperience designing and operating distributed systems or data pipelinesFamiliarity with async processing patterns, task queues, and worker pool architecturesSolid understanding of PostgreSQL and data modelingStrong software engineering fundamentals: testing, CI/CD, observability, reliabilityYou're outcome-oriented and comfortable navigating ambiguity to deliver results
Ideally:
Experience with ML infrastructure, model serving, or training pipelinesHands-on experience with Kubernetes in production environmentsFamiliarity with GPU workloads and the unique challenges of ML at scaleExperience with geospatial data formats (GeoTIFF, COG, STAC) or imagery processingBackground deploying systems in regulated or air-gapped environments
Tech Stack
Python, FastAPIPostgreSQL, RedisDocker, Kubernetes (EKS, K3S)AWS (with on-prem and edge deployment targets)GPU infrastructure for ML inference
