Engineer I, Software - Factory Team (R6235)

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

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.

Bachelor’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.