Staff Engineer, HMS Factory (R5276)

Team: Integration & Test

Location: Washington, DC

Commitment: Full Time Employee

Workplace Type: onsite

Job Description:

What you'll do:

Write, optimize, and maintain advanced Python-based data analysis pipelines and custom analyzers (such as the Shared Analysis Tool) to parse simulation runs, extract log data, and calculate mission performance metrics.
Develop, debug, and maintain custom C++ or Python adapters and automated test orchestration wrappers around core HM Forge to satisfy program-specific validation requirements.
Implement programmatic scenario-generation pipelines and write domain-randomization scripts to systematically test autonomy behaviors under highly varied environmental conditions.
Analyze simulation log files, rosbags, and network captures to isolate, reproduce, and debug autonomy software defects or simulator failures.
Leverage generative AI and agentic AI tooling to dramatically accelerate the development, refinement, and maintenance of analysis suites used to characterize performance and validate HMS solutions.
Build reusable agentic workflows that automate the generation and evolution of analysis suites, including automated discovery and interpretation of available test data and system outputs, intelligent identification of analysis needs, and development of new analyzers for HMS performance characterization and validation.
Build, test, and write Terraform or Kubernetes configuration files to prototype and deploy distributed orchestration templates on top of the Forge Server.
Write reusable, clear step-by-step developer guides, API documentation, and workspace templates to help program engineers implement automated simulation tests.
Design and evolve common telemetry ingestion, analysis, and reporting frameworks that enable consistent performance evaluation across HMS programs.

Required qualifications:

Typically requires a Bachelor's degree in Computer Science, Computer Engineering, Aerospace Engineering, Systems Engineering, or related technical discipline, or equivalent practical experience.
Strong software development background in Python, particularly for data analysis, scripting, and automation tooling (utilizing libraries like NumPy, Pandas, or similar).
Hands-on experience with automated test orchestration, CI/CD, simulation environments, and container-based architectures (Docker, Kubernetes).
Solid software engineering fundamentals, including version control (Git), automated testing, and disciplined debugging practices in a Linux environment.
Familiarity with distributed compute frameworks or container orchestration tools is highly preferred.
Strong technical problem-solving skills, with the ability to troubleshoot complex, asynchronous software systems.
Ability to obtain and maintain an active U.S. SECRET security clearance (U.S. citizenship required).