Why Castelion, Why Now

Castelion is moving incredibly fast to develop and deliver advanced defense systems at a time when execution matters more than ever. We believe focus, ownership, and excellence are decisive advantages - and we're building a world-class team to turn bold ideas into real capability.

This is a rare opportunity to join at an early stage, where you'll have significant ownership, collaborate with exceptional teammates, and make a direct, measurable impact on our mission and the future of the company - regardless of your function.

Cloud Compute Engineer

Castelion is seeking a Cloud Compute Engineer to build and own the Seeker Team’s scalable cloud compute platform for data processing, simulation, calibration, and machine learning. You will move compute-intensive workflows from individual laptops and one-off scripts into a shared, automated, and reliable platform that supports multiple engineering teams.

Working across data processing, ML training, and test infrastructure, you will build distributed compute systems, optimize Python workloads, and integrate simulation and regression testing into CI/CD pipelines. This is a hands-on role with significant impact on engineering productivity, data-processing speed, and the scalability of Castelion’s seeker development.

Responsibilities

Build a shared compute platform: Develop and operate distributed compute infrastructure supporting batch processing, custom jobs, simulation, calibration, preprocessing, and ML training.
Automate data processing: Build event-driven workflows that automatically process newly ingested data, including flight-test and chamber calibration data.
Scale compute-intensive workloads: Enable scalable ML training, Foxglove RDL preprocessing, data preparation, replay, and other user-submitted jobs.
Integrate compute with CI/CD: Enable simulation and regression tests to run automatically in CI/CD pipelines, expanding test coverage beyond standard runner limitations.
Optimize performance and cost: Profile and improve Python workloads, data flows, memory usage, concurrency, and resource utilization—not just the underlying infrastructure.
Improve the developer experience: Provide simple interfaces, APIs, or SDKs for engineers to submit, monitor, debug, and rerun jobs without managing infrastructure manually.
Ensure reliability and observability: Implement logging, metrics, alerting, job traceability, and cost monitoring to make failures easy to diagnose and recover from.
Enable reproducible workflows: Track code versions, inputs, configurations, and outputs so compute jobs can be reliably reproduced and audited.
Support large-scale data movement: Design efficient approaches to storage, data transfer, caching, partitioning, and preprocessing.
Drive adoption across teams: Work directly with engineers to understand their workflows and establish consistent compute patterns across unclassified and classified networks.

Basic Qualifications

Experience building and operating distributed compute platforms used by multiple engineering teams.
Strong Python skills, including profiling, concurrency, memory management, and performance optimization.
Production experience with Ray and Kubernetes.
Experience designing batch-processing, event-driven, and user-submitted job systems.
Experience with Azure compute, storage, networking, identity, and monitoring services.
Demonstrated ability to optimize application code and data pipelines for performance, reliability, and cost.
Experience implementing observability, automated job recovery, and reproducible processing workflows.
Ability to build simple developer interfaces and work directly with users to automate complex workflows.

Preferred Skills and Experience

Strong PyTorch experience, including distributed training, data loading, checkpointing, and GPU utilization.
Experience scaling ML training across large datasets and distributed compute resources.
Experience integrating simulation and regression workloads into CI/CD pipelines.
Familiarity with large-scale engineering datasets, flight-test data, calibration, or scientific computing workflows.