Engineer II, Software - Factory (R6217)

About the role

Responsibilities

• Develop, test, and maintain Python scripts and libraries used to analyze simulation data, extract log information, and calculate basic mission or system-performance metrics.
• Support the development of analysis tools that process simulation outputs, telemetry, rosbags, and other test artifacts.
• Help build and maintain automated test workflows and wrappers around HMS Forge and other internal test-orchestration tools.
• Assist with developing program-specific test adapters, configuration files, and automation utilities in Python and, when needed, C++.
• Contribute to programmatic scenario-generation tools that create varied simulation conditions for testing autonomy behaviors.
• Write and maintain domain-randomization scripts that vary environmental, vehicle, sensor, mission, or operational parameters across test runs.
• Investigate test failures and simulation issues by reviewing logs, configuration files, telemetry, rosbags, network captures, and automated test results.
• Help improve repeatability and consistency in simulation testing by maintaining reusable test templates, common analysis utilities, and standardized reporting outputs.
• Assist with containerized development and testing environments using tools such as Docker, Kubernetes, and related infrastructure tooling.
• Contribute to prototype configurations for distributed testing and batch simulation runs, including Terraform, Kubernetes manifests, or similar deployment configurations.
• Help maintain stable development workflows by supporting source-control practices, dependency updates, CI/CD pipelines, and software build troubleshooting.
• Create clear developer documentation, setup guides, API references, example workflows, and workspace templates for engineers using automated simulation-test tools.
• Participate in code reviews, design discussions, test reviews, and collaborative debugging sessions with autonomy, simulation, platform, and program teams.

Requirements

• Master’s degree in Computer Science, Computer Engineering, Aerospace Engineering, Systems Engineering, or a related technical discipline; equivalent practical experience may be considered.
• Foundational software-development experience in Python gained through coursework, internships, research, personal projects, or professional experience.
• Familiarity with Python scripting, automation, and data processing. Experience with libraries such as NumPy, Pandas, matplotlib, or similar tools is helpful.
• Familiarity with Linux development environments, including command-line tools, basic shell scripting, software installation, and debugging.
• Understanding of core software-engineering practices, including Git-based version control, code review, automated testing, and debugging.
• Familiarity with structured data formats and APIs, such as JSON, YAML, CSV, REST APIs, or command-line interfaces.
• Exposure to automated testing, CI/CD systems, simulation environments, robotics, autonomy, data pipelines, or developer tooling.
• Basic understanding of container concepts and tools such as Docker, Podman, or Kubernetes.
• Strong technical problem-solving skills, attention to detail, and a willingness to learn in a fast-moving, collaborative engineering environment.
• Ability to obtain and maintain an active U.S. SECRET security clearance; U.S. citizenship is required.
• Internship, academic project, research, or personal-project experience involving autonomous systems, robotics, simulation, software testing, data analysis, or distributed computing.
• Experience creating Python-based data-analysis workflows using NumPy, Pandas, SciPy, matplotlib, Plotly, or similar libraries.
• Exposure to continuous-integration systems such as GitHub Actions, GitLab CI, Jenkins, Buildkite, or similar tools.
• Familiarity with test frameworks such as pytest, unittest, GoogleTest, Robot Framework, or similar tools.
• Experience working with containers using Docker or Podman, including writing Dockerfiles and running local containerized applications.
• Exposure to Kubernetes, Terraform, Helm, or other infrastructure-as-code and container-orchestration tools.
• Familiarity with ROS, ROS 2, rosbag files, telemetry formats, robotics middleware, network diagnostics, or simulation logs.
• Familiarity with distributed compute frameworks or container orchestration tools is highly preferred.
• Familiarity with Python-based distributed-computing concepts and large-scale simulation workloads; exposure to Ray, task or actor-based parallelism, Monte Carlo testing, parameter sweeps, and Design of Experiments (DOE) is preferred.
• Experience creating Python-based data-analysis workflows using NumPy, Pandas, SciPy, matplotlib, Plotly, or similar libraries.
• Experience using Linux debugging and observability tools, such as grep, jq, tail, journalctl, tcpdump, Wireshark, or Python logging.
• Interest in generative AI, AI-assisted software development, agentic workflows, automated analysis, or developer productivity tooling.