Your Mission:
The AI Factory team is seeking an experienced Site Reliability Engineer to lead significant reliability improvements across the platforms and services that support AI development and deployment. You will turn recurring operational problems into scalable engineering solutions through software development, automation, observability, and reliability testing.
You will own a defined technical area within the reliability roadmap—from identifying and scoping work through design, implementation, and adoption. Working with platform, application, and operations teams, you will establish reusable approaches that improve reliability across the AI Factory rather than optimizing a single tool or service.
Key Responsibilities:
• Independently lead a significant reliability capability, such as incident-response automation, platform health and observability, release validation, or resilience testing.
• Identify recurring failure modes and operational toil; design and implement software, automation, and process improvements that address their underlying causes.
• Build and maintain operational tooling that helps teams validate system health, investigate issues, and recover more effectively.
• Lead and implement approaches for standardizing and correlating metrics, logs, and traces across services, improving how teams detect and troubleshoot reliability issues.
• Define and standardize approaches for validating upgrades, releases, and platform changes, including automated testing of failure and recovery scenarios.
• Define and standardize CI/CD, GitOps, and progressive-delivery practices across teams to reduce deployment risk.
• Lead technical reviews with partner teams, communicate reliability recommendations, and coordinate implementation across team boundaries.
• Establish reusable engineering patterns, review code and designs, and mentor less-experienced engineers.
• Contribute to reliability platforms such as Cluster Concierge while retaining ownership of broader reliability outcomes, not just a single product.
Responsible for autonomy hardware and software system integration and testing including verification and validation.Translates customer requirements into product and systems specifications while addressing technical, schedule, and cost considerations; Establishes functional and technical specifications and standards for autonomous systems; Determines sensing hardware components; Recommends and selects appropriate control systems; Integrates and optimizes the autonomous compute and sensing hardware and software; Solves hardware/software interface problems; Develops plan(s) to integrate autonomous functionality into product(s) and platform(s) and other system(s); Develops test plans for validation and verification and procedures for test and evaluation requirements in collaboration with designers and developers; Performs integration testing and coordinates subsystem and/or system testing activities for autonomous programs; Documents and conducts analysis of test results and recommends fixes to software, hardware components, subsystems and systems; Interfaces with other teams involved the development lifecycle for perception
• Experience designing, developing, and maintaining production software or automation using Go, Python, or a comparable language.
• Experience operating or engineering Kubernetes-based platforms, including troubleshooting complex service or infrastructure issues.
• Experience building or improving CI/CD, GitOps, infrastructure automation, or deployment workflows.
• Experience using observability data—including metrics, logs, or traces—to diagnose problems and improve system health.
• Demonstrated ability to own a technical initiative through design, implementation, and coordination with other teams.
• Experience with OpenShift, GitLab CI/CD, Argo CD, Argo Rollouts, or similar GitOps and progressive-delivery tooling.
• Experience with Prometheus, Grafana, OpenTelemetry, or comparable observability tools.
• Experience designing automated reliability tests, upgrade validation, resilience tests, or failure-mode analyses.
• Experience improving incident response, reducing operational toil, or defining actionable service-health measures.
• Experience developing internal platforms or tools used by multiple engineering teams.
• Familiarity with AI/ML platforms, GPU-based infrastructure, or deployments in disconnected environments.
• Experience mentoring engineers and establishing reusable development or operational practices.
• Strong oral and written communication skills, and ability to collaborate with cross-functional partners
• Creative and resourceful when it comes to problem-solving
• Ability to work with internal stakeholders to collect feedback, prioritize tasks, and manage the engineering backlog
• Self-motivated, self-directed, and the ability to thrive in a fast-paced environment in an industry that constantly changes
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): $122600.00 - $227600.00
- 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): $110300.00 - $204900.00
- Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc.
- Full-time salary range (GEOZONE 3): $98100.00 - $182100.00
- Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc.
- Full-time salary range (GEOZONE 4): $88300.00 - $163900.00
- 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 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.
Skills Required
- Experience designing, developing, and maintaining production software or automation using Go, Python, or a comparable language.
- Experience operating or engineering Kubernetes-based platforms and troubleshooting complex service or infrastructure issues.
- Experience building or improving CI/CD, GitOps, infrastructure automation, or deployment workflows.
- Experience using metrics, logs, or traces to diagnose problems and improve system health.
- Ability to own a technical initiative through design, implementation, and coordination with other teams.
- Experience with OpenShift, GitLab CI/CD, Argo CD, Argo Rollouts, or similar tooling.
- Experience with Prometheus, Grafana, OpenTelemetry, or comparable observability tools.
- Experience designing automated reliability, upgrade validation, resilience, or failure-mode tests.
- Experience improving incident response, reducing operational toil, or defining service-health measures.
- Experience developing internal platforms or tools used by multiple engineering teams.
- Familiarity with AI/ML platforms, GPU infrastructure, or disconnected-environment deployments.
- Experience mentoring engineers and establishing reusable development or operational practices.
- Strong oral and written communication and cross-functional collaboration skills.
- Ability to collect stakeholder feedback, prioritize tasks, and manage an engineering backlog.
- Self-motivated, self-directed, and able to thrive in a fast-paced environment.
Lockheed Martin Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Lockheed Martin and has not been reviewed or approved by Lockheed Martin.
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Retirement Support — Retirement benefits are described as a standout element, with a strong 401(k) program and options like an employee stock purchase plan. Feedback suggests these features meaningfully enhance total compensation and long‑term financial security.
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Healthcare Strength — Healthcare coverage is portrayed as comprehensive from day one, with multiple plan options, mental health resources, and virtual care. Wellness supports such as on‑site services and preventive programs are also highlighted.
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Flexible Benefits — Work arrangements often include compressed schedules and telework, contributing to a favorable work‑life balance. Feedback suggests this flexibility is a key reason many view the overall package positively.
Lockheed Martin Insights
What We Do
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