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About This Role
JOB ID: 729788BR
Date posted: Jun. 01, 2026
Description:
Global Production Operations is hiring a Senior Manager to lead our OpsDev team, responsible for identifying \& injecting Application / Ai /ML / Data Science solutions into our operations environments.
OpsDev is a multi\-disciplinary team consisting of applications engineers, AI/ML/Data science engineers and embedded Forward Deployed Engineers (FDEs).
This role will have a particular focus on supporting the development of products by working with stakeholders to understand their business needs, translate them into data problems, and then set the direction to solve those problems. This individual must drive the work from prototype through to completion and delivery of a minimum viable product or final product so that the business users capture value from the work of the team.
The ideal candidate will have strong focus on the end user experience, ensuring appropriate translation of requirements across different functional vernaculars.
What’s In It For You:
From onsite to remote, we offer flexible work schedules to comprehensive benefits investing in your future and security,
Do you want to be part of a company culture that empowers employees to think big, lead with a growth mindset, and make the impossible a reality? We provide the resources and give you the flexibility to enable inspiration and focus \-if you have the passion and courage to dream big, work hard, and have fun doing what you love then we want to build a better tomorrow with you.
Who You Are
You are a multi\-faceted teammate able to communicate and function effectively on an engineering team to create a collaborative environment that allows for the establishment of mission goals. Self\-motivated and inspired, you thrive in an environment where you are empowered to work your craft, never settling for the bare minimum.
Basic Qualifications:
- Bachelor’s Degree in Engineering, Computer Science from an accredited college in a related discipline
- Applicant MUST have experience managing an engineering or development team
- Expert proficiency in SQL, Python and/or R for data analysis and modeling
- Strong business acumen and ability to translate ambiguous business problems into analytical frameworks
- Experience working with cloud platforms (AWS, GCP, or Azure)
- Experience leveraging frontier Ai models to augment / accelerate / replace human workflows
- Excellent communication and interpersonal skills
Desired Skills:
- Experience with the integration of data products into customer applications
- Familiarity with MLOps practices, CI/CD for ML, and model monitoring frameworks (MLflow, Kubeflow, SageMaker, etc.)
- Experience with NLP, computer vision, or generative AI / LLM applications
- Familiarity with data visualization tools (Tableau, Power BI, Looker)
- Exposure to big data technologies (Spark, Hadoop, Kafka)
- Experience working in Agile / Scrum environments
Security Clearance Statement:
This position requires a government security clearance, you must be a US Citizen for consideration.
Clearance Level:
Secret
Other Important Information You Should Know
Expression of Interest:
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.
Ability to Work Remotely:
Onsite Full\-time: The work associated with this position will be performed onsite at a designated Lockheed Martin facility.
Work Schedules:
Lockheed Martin supports a variety of alternate work schedules that provide additional flexibility to our employees. Schedules range from standard 40 hours over a five day work week while others may be condensed. These condensed schedules provide employees with additional time away from the office and are in addition to our Paid Time off benefits.
Schedule for this Position:
4x10 hour day, 3 days off per week
Lockheed Martin is an equal opportunity employer. Qualified candidates will be considered without regard to legally protected characteristics.
The application window will close in 90 days; applicants are encouraged to apply within 5 \- 30 days of the requisition posting date in order to receive optimal consideration.
At Lockheed Martin, we use our passion for purposeful innovation to help keep people safe and solve the world's most complex challenges. Our people are some of the greatest minds in the industry and truly make Lockheed Martin a great place to work.
With our employees as our priority, we provide diverse career opportunities designed to propel, develop, and boost agility. Our flexible schedules, competitive pay, and comprehensive benefits enable our employees to live a healthy, fulfilling life at and outside of work. We place an emphasis on empowering our employees by fostering an inclusive environment built upon integrity and corporate responsibility.
If this sounds like a culture you connect with, you’re invited to apply for this role. Or, if you are unsure whether your experience aligns with the requirements of this position, we encourage you to search on Lockheed Martin Jobs, and apply for roles that align with your qualifications.
Experience Level:
Experienced Professional
Business Unit:
AERONAUTICS COMPANY
Relocation Available:
Possible
Career Area:
Manufacturing
Type:
Full\-Time
Shift:
First
At Lockheed Martin, we apply our passion for purposeful innovation to keep people safe and solve the world's most complex challenges.
Pioneering Defense Technology: From aerospace to outer space to cyber space, you can innovate mission solutions alongside the best minds in the business.
United By Culture: Excellence, integrity, and collaboration define us. We accelerate change and embrace one another’s perspectives to win for our customers.
Real Impact, Real Growth: Grow your career and skills for life. Our wide array of opportunities and technology\-driven learning programs enable your development and agility.
Your Health, Your Wealth, Your Life: Competitive pay, comprehensive benefits and flexible schedules designed so you thrive — at work and beyond.
Empowered to Be Your Best: Use your strengths to make a difference in the lives of one another, our customers, our communities, and our planet.
Here, the possibilities are endless because we offer:
Flexible Schedules, dependent on role
Levels: Student, Entry, Mid, Senior, Management
Locations: Nationwide \& OCONUS Positions
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Lockheed Martin, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lockheed Martin AI Hiring
Lockheed Martin has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Engineer. Positions span Bethesda, MD, US, Manassas, VA, US, Fort Worth, TX, US. Compensation range: $170K - $311K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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