Computer Vision Analyst II - Top Secret clearance

Lockheed Martin•Littleton, CO

2d•Onsite

About The Position

Space is a critical domain, connecting our technologies, our security, and our humanity. While others view space as a destination, we see it as a realm of possibilities, where we can do more — we can innovate, invest, inspire, and integrate our capabilities to transform the future. At Lockheed Martin Space, we aim to harness the full potential of space to cultivate innovation, reduce costs, and push the boundaries of what technology can achieve. We’re creating future-ready solutions, focusing on resiliency and urgency through our 21st Century Security® vision. We’re erasing boundaries and forming partnerships across industries and around the world. We’re advancing spacecraft and the workforce to fuel the next generation. And we’re reimagining how space can connect us, ensuring security and prosperity. Join us in shaping a new era in space and find a career that's built for you. You’ll collaborate with some of the brightest minds in engineering to design and integrate pioneering capabilities that shape the future of our mission. Our high‑impact, cross‑functional team delivers cutting‑edge perception solutions for a variety of mission‑critical platforms. We specialize in classical computer‑vision algorithms—feature detection, stereo matching, optical flow—while also integrating modern AI techniques such as semantic segmentation and learned feature representations. Every solution is engineered with the strict hardware constraints of resource‑limited processors in mind, including the ultra‑low‑power, radiation‑hardened CPUs typical of space‑borne systems. We are seeking a motivated engineer to develop and support the end to end digital simulation platform. The ideal candidate will: Operate and update the simulation Conduct statistical analysis & uncertainty quantification via Monte‑Carlo simulations, Design‑of‑Experiments (DoE), sensitivity testing, and algorithm‑level error modeling Develop interactive dashboards, generate plots/reports for stakeholders, and tailor visual output for different audiences Define test cases, compare simulation results against analytical/ground‑truth data, document V&V evidence, and support formal review packages Debug computer‑vision algorithm performance in the simulation by debugging the underlying code and using visualizations to diagnose issues Collaborate with embedded‑hardware, optics, and systems teams to ensure the simulation reflects actual hardware behavior and integrates smoothly with downstream toolchains.

Requirements

Proficiency in Python (including libraries such as NumPy, SciPy, Pandas, and Bokeh)
Understanding of Statistical Methods (Monte‑Carlo, basic uncertainty quantification)
Ability to generate clear visualizations and communicate results to both technical and non‑technical audiences
Experience with Linux development environments and shell scripting
Proficiency in C++, including the ability to read, maintain, and/or extend C++ codebase
U.S. Citizen
Active Top Secret security clearance is necessary for consideration and is required prior to start.

Nice To Haves

Bachelor’s degree in Computer Science, Aerospace Engineering, Mathematics, or a related technical field (or equivalent professional experience) with 1+ year(s) of relevant experience
Experience with MATLAB
Foundational knowledge of core algorithms used in computer‑vision and signal‑processing (e.g., feature extraction, optical flow, SLAM, matrix factorization, optimization, linear‑algebra routines)
Docker - experience containerizing applications for reproducible builds and testing
CI/CD pipelines - familiarity with GitLab CI or similar tools for automated testing and deployment

Responsibilities

Operate and update the simulation
Conduct statistical analysis & uncertainty quantification via Monte‑Carlo simulations, Design‑of‑Experiments (DoE), sensitivity testing, and algorithm‑level error modeling
Develop interactive dashboards, generate plots/reports for stakeholders, and tailor visual output for different audiences
Define test cases, compare simulation results against analytical/ground‑truth data, document V&V evidence, and support formal review packages
Debug computer‑vision algorithm performance in the simulation by debugging the underlying code and using visualizations to diagnose issues
Collaborate with embedded‑hardware, optics, and systems teams to ensure the simulation reflects actual hardware behavior and integrates smoothly with downstream toolchains