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Shield AI is a venture-backed defense-tech company developing intelligent autonomy systems, aircraft, and simulation technologies to protect service members and civilians. This Engineer II role focuses on building and maintaining the software infrastructure that powers simulation-based testing and analysis for autonomous systems.
You will develop Python scripts and libraries to analyze simulation data, extract logs, and calculate performance metrics. Key responsibilities include supporting analysis tools that process simulation outputs and telemetry; building automated test workflows and wrappers around internal test-orchestration platforms; developing test adapters and automation utilities in Python and C++; and creating programmatic scenario-generation tools that vary environmental and operational parameters for testing autonomy behaviors.
You'll investigate test failures by reviewing logs, telemetry, and automated results; improve testing repeatability through reusable templates and standardized reporting; work with containerized environments (Docker, Kubernetes); contribute to distributed testing configurations using Terraform and Kubernetes manifests; maintain CI/CD pipelines and development workflows; and create developer documentation and setup guides. You'll participate actively in code reviews, design discussions, and collaborative debugging with autonomy, simulation, platform, and program teams.
This role requires strong technical problem-solving, attention to detail, and the ability to thrive in a fast-moving, collaborative engineering environment. You must be able to obtain and maintain a U.S. SECRET security clearance; U.S. citizenship is required.
**Requirements:**
- Master's degree in Computer Science, Computer Engineering, Aerospace Engineering, Systems Engineering, or related technical discipline (equivalent practical experience may be considered)
- Foundational software-development experience in Python through coursework, internships, research, personal projects, or professional work
- Familiarity with Python scripting, automation, and data processing; experience with NumPy, Pandas, matplotlib, or similar libraries is helpful
- Familiarity with Linux development environments, command-line tools, basic shell scripting, software installation, and debugging
- Understanding of core software-engineering practices: Git-based version control, code review, automated testing, and debugging
- Familiarity with structured data formats and APIs (JSON, YAML, CSV, REST APIs, CLIs)
- Exposure to automated testing, CI/CD systems, simulation environments, robotics, autonomy, data pipelines, or developer tooling
- Basic understanding of container concepts and tools (Docker, Podman, Kubernetes)
- Strong technical problem-solving skills and willingness to learn in a collaborative environment
- Ability to obtain and maintain active U.S. SECRET security clearance; U.S. citizenship required
**Preferred Qualifications:**
- Internship, academic project, research, or personal-project experience with 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
- Exposure to CI systems (GitHub Actions, GitLab CI, Jenkins, Buildkite)
- Familiarity with test frameworks (pytest, unittest, GoogleTest, Robot Framework)
- Experience with Docker or Podman, including writing Dockerfiles
- Exposure to Kubernetes, Terraform, Helm, or infrastructure-as-code tools
- Familiarity with ROS, ROS 2, rosbag files, telemetry formats, robotics middleware, or simulation logs
- Experience with distributed compute frameworks or container orchestration
- Familiarity with Python-based distributed-computing concepts, large-scale simulation workloads, Ray, task/actor-based parallelism, Monte Carlo testing, parameter sweeps, or Design of Experiments
- Experience with Linux debugging and observability tools (grep, jq, tail, journalctl, tcpdump, Wireshark, Python logging)
- Interest in generative AI, AI-assisted software development, agentic workflows, or developer productivity tooling
