AI/ML Engineering Manager (Signal Processing)

Standard Job Description Join the Lockheed Martin Artificial Intelligence Center (LAIC) team and be part of a dynamic group that's pushing the boundaries of innovation in electronic warfare, signal processing, data science, artificial intelligence, and machine learning. As a key member of the Applied AI team, you'll have the opportunity to guide the development of advanced sensor-level AI capabilities across a wide range of mission-focused projects. With a focus on technical excellence, functional leadership, rapid iteration, and deployment, you'll help develop teams and capabilities that make a real impact across the Lockheed Martin enterprise. What you'll be doing:

• Leading the development and transition of AI and machine learning capabilities for sensor-level applications supporting Lockheed Martin radar, communications, electronic warfare, and signal processing systems
• Establishing and maintaining technical roadmaps that align sensor-level AI investments, capability development, and program needs
• Providing functional leadership through team development, staffing plans, workload management, mentoring, and coordination of technical assignments
• Supporting technical proposals, white papers, customer engagements, and internal investments by shaping credible AI/ML approaches and execution plans
• Collaborating with program, systems engineering, software, and customer teams to address evolving mission requirements

Who are you: You are a motivated, professional, and independent leader with experience applying artificial intelligence and machine learning to signal processing or related sensing systems. You possess:

• Strong leadership and organizational skills, with the ability to develop technical roadmaps, balance staffing demands, and guide teams toward program and portfolio objectives
• A strong technical foundation in AI/ML model development, evaluation, integration, and deployment for signal-processing or sensing applications
• Excellent interpersonal and communication skills, with the ability to build productive relationships across engineering teams, program leadership, and customer organizations
• A proactive, accountable approach to identifying opportunities, advancing innovative capabilities, and delivering high-quality results
• The ability to work on-site at major Lockheed Martin facilities

US citizenship is required for this role #LMLAIC

Basic Qualifications

• Bachelor’s degree in engineering, computer science, applied mathematics, physics, or a related technical discipline
• Experience with radar, communications, electronic warfare, sonar, or comparable sensing systems
• Experience developing, integrating, or deploying machine-learning or deep-learning capabilities using frameworks such as PyTorch, TensorFlow, JAX, Keras, or equivalent tools
• Experience leading technical teams, workstreams, or projects supporting the development, integration, test, or transition of mission capabilities
• Experience developing technical roadmaps, supporting proposals or white papers, and communicating technical approaches, plans, and results to program leadership, stakeholders, or customers

Desired Skills

• Advanced degree in engineering, computer science, applied mathematics, physics, or a related technical discipline
• Experience providing functional leadership, including staffing plans, workload management, mentoring, performance development, or technical assignment coordination
• Experience applying artificial intelligence, machine learning, or deep learning to sensor-level signal-processing or sensing applications, including radar, communications, electronic warfare, sonar, audio, biomedical sensing, or related domains
• Experience with RF, digital signal processing, and analog signal-processing fundamentals
• Experience integrating AI/ML capabilities into mature, complex, mission-critical software systems
• Experience with multiple sensing modalities, including passive and active RF, EO/IR, Synthetic Aperture Radar (SAR), or novel sensing techniques
• Experience with model validation, reproducible experimentation, data and model versioning, automated test, and ML lifecycle practices
• Experience preparing and presenting technical briefings for government customers
• Familiarity with software-defined radios or RF hardware
• Experience with C++, Rust, or other compiled languages
• Active Secret clearance with the ability to obtain a Top Secret clearance

Pay Information Full-Time Salary Range: $166900.00 - $309900.00 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 programs may be offered depending on the position. Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics.
• For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.