As an Software Engineer I on the HMS Factory Team, you will help build and maintain automated testing, log-analysis, and scenario-generation tools that support the Hivemind Platform (HMP). You will work with experienced software, autonomy, and systems engineers to help teams evaluate AI Pilot behaviors through repeatable simulation testing and data-driven performance analysis.
This is a hands-on engineering role for an early-career developer interested in autonomy, simulation, software test infrastructure, data analysis, and distributed systems. You will contribute Python-based tooling, test automation scripts, analysis utilities, and documentation that help autonomy and systems developers run tests, understand simulation results, and identify software issues.
You will also gain exposure to secure and commercial development environments, build and dependency-management workflows, containerized infrastructure, and distributed test orchestration. Under guidance from senior engineers, you will help improve the reliability, usability, and scalability of the team’s testing and analysis ecosystem.
What you'll do:
- 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.
Required qualifications:
- 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.
Preferred qualifications:
- 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.
Skills Required
- 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 through coursework, internships, research, personal projects, or professional experience.
- Familiarity with Python scripting, automation, and data processing.
- Familiarity with Linux development environments, command-line tools, basic shell scripting, software installation, and debugging.
- Understanding of Git-based version control, code review, automated testing, and debugging.
- Familiarity with 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 Docker, Podman, Kubernetes, or similar container technologies.
- Ability to obtain and maintain an active U.S. SECRET security clearance.
- U.S. citizenship.
- Experience involving autonomous systems, robotics, simulation, software testing, data analysis, or distributed computing.
- Experience with Python data-analysis workflows using NumPy, Pandas, SciPy, matplotlib, Plotly, or similar libraries.
- Exposure to GitHub Actions, GitLab CI, Jenkins, Buildkite, or similar continuous-integration systems.
- Familiarity with pytest, unittest, GoogleTest, Robot Framework, or similar test frameworks.
- Experience with Docker or Podman, including Dockerfiles and local containerized applications.
- Exposure to Kubernetes, Terraform, Helm, or similar infrastructure-as-code and orchestration tools.
- Familiarity with ROS, ROS 2, rosbag files, telemetry formats, robotics middleware, network diagnostics, or simulation logs.
- Familiarity with distributed computing, Ray, parallelism, Monte Carlo testing, parameter sweeps, or Design of Experiments.
- Experience with 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.
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Company materials highlight comprehensive medical, dental, and vision coverage with mental-health support and an EAP. Third-party benefits summaries also portray health coverage as a strong component of the package.
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Retirement Support — Offerings include a 401(k) with employer matching, cited on the careers page and independent benefits directories. Multiple sources indicate a match exists, though exact formulas have varied over time and warrant confirmation.
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Equity Value & Accessibility — Company communications describe equity for all full-time hires and stock benefits as part of total rewards. Independent listings also note company equity/RSUs as a standard element.
Shield AI Insights
What We Do
At Shield AI, you won't wait years to see your work reach the field. You'll build hardware and software that operates in the real world right now, in the hands of the people who depend on it. Hivemind, our AI pilot, has been flying since 2018. It has flown more than 30 platforms, including an F-16, and it now sits under a U.S. Air Force production contract for Collaborative Combat Aircraft. When you write code or shape a system here, you contribute to technology with a proven flight record and a clear production future. V-BAT flies intelligence, surveillance, and reconnaissance missions with an operational record that stretches from Ukraine to the Indo-Pacific. It delivers eyes where they matter most, in the most demanding conditions on earth. The teams behind it watch their work get tested where the stakes are real. X-BAT takes its first flight this year. It's an AI-piloted fighter that needs no runway, built to operate where traditional aircraft can't. Join now and you help shape a program at its earliest, most formative stage. That's the kind of ground-floor work that defines a career. Do the most impactful work of your life, on problems that matter. Autonomy at this level asks a lot of you. You'll take on problems in perception, planning, and control that few teams anywhere are equipped to solve. You'll work across disciplines, from aerospace and robotics to machine learning and systems engineering, alongside people who hold themselves to an exacting standard and expect the same from you. Our mission is clear: protect service members and civilians with intelligent systems. That purpose runs through every decision, every design review, and every deployment. It's why the work here carries a weight you can feel. Ready to join our mission? Explore our open roles and find where you fit.
Why Work With Us
Founded in 2015 by a former Navy SEAL, Shield AI builds AI pilots and uncrewed aircraft. Veterans aren't an afterthought here, they're at every level. It's why the work carries weight: AI pilots and uncrewed aircraft flying real missions, from Ukraine to the Indo-Pacific, protecting service members and civilians.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
