AI Research Engineer Stf - Engineering Transformation
Lockheed Martin Corporation · United States · 2026-10-30
About this role
Standard Job Description
Strategic Product Development within Engineering Transformation is seeking a highly skilled Staff AI/ML Engineerto join a cross-functional team advancing the Engineering Generative AI portfolio.
What You Will Be Doing:
In this role, you will apply modern AI tools and techniques to solve complex Engineering challenges and develop scalable, governed AI capabilities.
This role offers the opportunity to work directly with Engineering organizations and their functional partners to scope opportunities, develop prototypes, and mature validated solutions for enterprise use. Ideally, you will have advanced experience in applied AI and ML, research, development and sustainment of Generative AI applications, and software engineering.
Key Responsibilities will include the following:
• Working with Engineering users and cross-functional teams to identify high-value AI opportunities and translate workflows into technical requirements and deployable solutions.
• Researching, developing, testing, deploying, and sustaining Generative AI, AI/ML, and agentic applications that improve Engineering ways of working.
• Designing and implementing AI algorithms, applications, and services, including LLM-based retrieval, tool use, orchestration, and decision-support workflows.
• Building and optimizing full-stack AI solutions, including user experiences, APIs, backend services, data connections, model integrations, and deployment pipelines.
• Developing prototypes using approved AI-assisted development tools and mature successful capabilities into secure, tested, documented, enterprise-deployable solutions.
• Applying Responsible AI Governance and Ethics (RAGE), cybersecurity, architecture, and DevSecOps requirements throughout development, testing, deployment, and sustainment.
• Evaluating solution performance, reliability, safety, and user value through AI evaluation, automated testing, regression testing, monitoring, and user feedback.
• Partnering with the AI Portfolio Chief Architect, Engineering Knowledge & Data Integration Architect, Data and AI Enablement, IT Applications, and Engineering Function teams to establish sustainable solutions and integrations.
Further Information About This Opportunity:
This role is fully remote. US Citizenship is required due to system access.
Basic Qualifications
• Hands-on experience developing and deploying AI/ML solutions at scale.
• Experience with AI/ML programming languages, such as Python or C++, and software engineering practices for production applications.
• Hands-on experience developing, fine-tuning, integrating, or deploying large language models and Generative AI applications.
• Demonstrated expertise in prompt engineering, including designing, testing, and optimizing prompts for reliability, safety, and performance.
• Experience deploying or hosting AI solutions and applications in production environments.
• Bachelor's degree in a STEM discipline, such as Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Data Science, or a related field.
• US Citizenship is required due to system access
Desired Skills
• Experience designing and implementing AI agents, planning or search approaches, tool-use APIs, multi-agent systems, or agentic workflows.
• Experience with frameworks supporting AI agent orchestration, such as LangGraph, CrewAI, AutoGen, or LlamaIndex.
• Experience implementing RAG, hybrid retrieval, semantic chunking, GraphRAG, knowledge graphs, or vector search.
• Experience building Model Context Protocol (MCP) clients or servers to connect AI models, data, and tools.
• Experience with AWS services and cloud-native infrastructure used to build and secure scalable AI applications.
• Experience with AI evaluation, observability, guardrails, responsible AI, model-risk management, or RAGE-aligned practices.
• Familiarity with Engineering tools and data, including PLM, requirements management, systems engineering, configuration management, software engineering, manufacturing, or quality.
Pay Information
GeoZone Definition: GeoZones are geographic groupings created by Lockheed Martin to align compensation ranges with regional labor markets and cost-of-labor differences across the United States. Locations are assigned a Geo Zone based on the primary work location of the role.
• Full-time salary range (GEOZONE 1): $150800.00 - $280000.00
• Includes metropolitan areas such as Sunnyvale CA; Pal Alto, CA; New York City metropolitan area; Newark, New Jersey; etc.
• Full-time salary range (GEOZONE 2): $135700.00 - $251900.00
• Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc.
• Full-time salary range (GEOZONE 3): $120600.00 - $224000.00
• Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc.
• Full-time salary range (GEOZONE 4): $108600.00 - $201600.00
• Includes metropolitan areas such as Camden, AR; Lexington, KY; Lufkin, TX; etc.
At Lockheed Martin, we know mission success starts with taking care of our people. Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow—both professionally and personally.
The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only.
Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer.
Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability.
• Annual short-term and/or long-term incentive compensation…
Skills asked for
- llm
- cybersecurity
- python
- c++
- data science
- aws
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