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ABOUT THE ROLE We are seeking a Data Infrastructure Engineer to build and operate the infrastructure that turns drone, aerial, and orbital sensing data into production datasets, models, and customer-facing insights. This role spans ingestion, processing, storage, compute, and serving, with a strong emphasis on reliability, observability, performance, and cost. You will work closely with research and product engineering to shorten iteration cycles, improve reproducibility, and raise the quality bar for production systems. You will define clear interfaces and operational standards that keep the platform trustworthy as data volume, model complexity, and product usage scale. WHAT YOU’LL DO - Design, build, and operate scalable data and ML infrastructure on AWS, including workloads running on Kubernetes - Build and maintain systems for ingestion, processing, storage, and serving, with strong guarantees around data quality, correctness, and operational safety - Partner closely with research to support perception model training and evaluation workflows, enabling faster experimentation and more reproducible iteration - Build platform primitives for observability, data versioning, lineage, evaluation, reproducibility, and operational excellence - Partner with product engineering to ensure data- and model-derived insights are accessible through reliable, low-latency serving and retrieval interfaces - Design systems that enable efficient access patterns for customer-facing products, including search, indexing, and large-scale querying - Identify and address bottlenecks in throughput, cost, and operational complexity as the platform scales WHAT WE’RE LOOKING FOR You have strong software engineering fundamentals and have built production systems where reliability, cost, and performance matter. You can reason clearly about distributed systems tradeoffs, and you have experience designing data-intensive infrastructure that other engineers depend on. You are comfortable working across data platform and ML platform concerns, and you understand how tightly coupled they become in production. You care about reproducibility, debuggability, and developer experience because you have seen how quickly they become bottlenecks. You work effectively across research and product teams. You can translate ambiguous needs into clear interfaces and systems, and you can drive work from design through production while maintaining a high quality bar. A few things we expect in this role: - Meaningful experience building production data infrastructure, ML infrastructure, or distributed systems - Strong programming skills in Python and SQL, with the judgment to choose the right abstractions and interfaces for production systems - Experience building and operating systems on AWS - Familiarity with modern infrastructure and platform tooling, including Kubernetes, Docker, and Terraform - Experience working with production storage and serving systems such as Postgres and Redis - Familiarity with data and ML workflow tooling such as Metaflow - Strong instincts for observability, testing, and operational excellence NICE TO HAVE - Experience supporting ML training, evaluation, batch inference, or model deployment in production - Familiarity with modern large-scale data patterns and tooling, including streaming, backfills, partitioning strategy, and schema evolution - Experience building internal platform primitives such as data versioning and lineage, dataset curation, experiment tracking, or tooling for reproducible workflows - Exposure to perception, multimodal, or geospatial systems, especially where data originates from real sensors and is used in real products LOCATION This is a full-time role based in San Francisco, CA. ITAR REQUIREMENTS To comply with U.S. export regulations, applicants must be one of the following: - A U.S. citizen or national - A lawful permanent resident (green card holder)