Computer Vision Engineer (Level 2 - 5)

Lockheed Martin•Littleton, CO

1d•$89,300 - $177,905•Onsite

About The Position

We are hosting a Lockheed Martin Space in-person hiring event in mid-April 2026. This event will be by invitation only and will take place near or in Littleton, CO. The actual date and location will be disclosed at a later date. Please apply to be considered. We are hiring levels 2-5. If you are interested in joining Lockheed Martin as a Systems Integration engineer, please apply to this requisition. This hiring event is for experienced professionals with an active security clearance. This event is for external applicants only. By applying to this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified you may be contacted for this and future openings. About Lockheed Martin Space: 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. About the Team: 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. About the Role: We are seeking a Computer Vision Engineer who can proactively drive architectural evolution and deliver high‑performance perception code for edge‑deployed platforms. The ideal candidate will: Apply solid software design principles (modularity, testability) to enhance and extend our existing C++/Python codebase Collaborate closely with hardware engineers, optics specialists, system architects, and other stakeholders to transform algorithmic concepts into high‑performance, deployable solutions Write production‑grade C++ code for core functionality, complemented by Python scripts for testing, simulation, and rapid prototyping. Deeply understand the target hardware architecture and its processing constraints to fine‑tune algorithms, maximizing runtime efficiency and extracting every ounce of performance from the processor Contribute to and leverage our Continuous Integration/Continuous Deployment pipelines using tools such as GitLab CI, Google Test, and Docker for automated builds, containerized testing, and seamless delivery.

Requirements

Demonstrated years of hands-on experience developing and deploying AI/ML solutions at scale.
Experience in a technical lead role for teams developing AI and software solutions.
Experience with AI/ML programming languages (Python, C++, etc.)
Demonstrated  years of hands‑on experience developing, fine‑tuning, or deploying large language models (≥ 10 B parameters).
Demonstrated expertise in prompt engineering (designing, testing, and optimizing prompts for reliability, safety, and performance).
Bachelor's degree in a STEM or related discipline (e.g. Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Data Science, etc.)
Advanced research experience in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, or Image Processing.
Experience deploying/hosting AI solutions & applications into production.
Successful applicants generally have a minimum of 2+ years of related experience
This position requires a government security clearance, you must be a US Citizen for consideration.

Nice To Haves

Design and implementation of autonomous agents (hierarchical RL, planning‑as‑search, LLM‑driven agents).
Experience with multi‑modal perception loops, self‑reflection, and tool‑use APIs (e.g., ReAct, LangChain Agents).
In-depth experience with AWS core services (EC2, S3, IAM, VPC, KMS, Lambda, API Gateway) to build and secure scalable infrastructure.
Proficiency with frameworks that support autonomous reasoning and planning, such as LangGraph, CrewAI, AutoGen, and LlamaIndex.
Ability to implement hybrid retrieval, semantic chunking, and GraphRAG (combining knowledge graphs with vector search).
Proficiency in building both MCP Clients (to host models) and MCP Servers (to expose data/tools)
Experience with AI Factory Platform

Responsibilities

Apply solid software design principles (modularity, testability) to enhance and extend our existing C++/Python codebase
Collaborate closely with hardware engineers, optics specialists, system architects, and other stakeholders to transform algorithmic concepts into high‑performance, deployable solutions
Write production‑grade C++ code for core functionality, complemented by Python scripts for testing, simulation, and rapid prototyping.
Deeply understand the target hardware architecture and its processing constraints to fine‑tune algorithms, maximizing runtime efficiency and extracting every ounce of performance from the processor
Contribute to and leverage our Continuous Integration/Continuous Deployment pipelines using tools such as GitLab CI, Google Test, and Docker for automated builds, containerized testing, and seamless delivery.