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About This Role
Standard Job Description
Position Overview
This individual blends deep technical expertise with strategic leadership to shape the vision for GenAI and autonomous‑agent solutions that optimize Corporate Procurement Operations. In addition to hands‑on development, you will drive stakeholder conversations, define solution roadmaps, and mentor junior engineers to elevate the overall capability of the SCI-Helix team.
Key Responsibilities
You will lead the end‑to‑end creation of autonomous GenAI workflows, translating high‑level business objectives from procurement leaders into technically sound designs that can be operationalized across the enterprise. By partnering directly with stakeholders—including category managers, sourcing analysts, and senior business leaders—you will gather requirements, clarify expectations, and articulate realistic delivery timelines. Leveraging your expertise, you will architect prompt strategies, model selection approaches, and tool integration patterns that enable agents to retrieve data, generate actionable insights, and execute downstream actions with minimal human intervention. Your role includes overseeing fine‑tuning initiatives, establishing reproducible training pipelines, and setting rigorous evaluation standards that address accuracy, bias, and hallucination risks. You will champion best practices for performance optimization, cost management, and secure deployment, ensuring that all solutions meet Lockheed Martin’s reliability, latency, and compliance thresholds. As a technical leader, you will mentor junior developers, conduct code reviews, and facilitate knowledge‑sharing sessions to build a robust, collaborative AI engineering culture. Documentation of architectures, prompt libraries, configuration settings, and test outcomes will be maintained in the team’s knowledge base, and you will serve as the primary point of contact for audit and governance reviews.
Why Join Us?
- Strategic Influence: Shape the future of AI‑enabled procurement across a global aerospace leader.
- Technical Excellence: Work with SOTA generative models and autonomous‑agent frameworks in a high‑security environment.
- Leadership Impact: Lead cross‑functional teams, mentor the next generation of AI engineers, and drive enterprise‑wide AI adoption.
- Career Growth: Continuous exposure to cutting‑edge research, mentorship from senior technical leaders, and opportunities to influence corporate AI strategy.
Basic Qualifications
- Bachelor’s (or higher) degree in Computer Science, AI, Data Science, Electrical Engineering, or a related field.
- Advanced proficiency in Python and experience building production‑grade AI services.
- Proven track record designing, fine‑tuning, and deploying LLM solutions at scale.
- Experience with RAG and vector store technologies.
- Demonstrated ability to architect and implement autonomous agent pipelines that integrate with enterprise data sources and tools.
- Experience leading technical discussions with business stakeholders, translating requirements into actionable solution plans
- 5 + years of hands‑on AI/ML engineering experience, with at least 2 years in a senior or lead capacity.
- Strategic thinking combined with a hands‑on mindset.
- Excellent written and verbal communication for stakeholder engagement.
- Ability to prioritize multiple initiatives while maintaining high quality
Desired Skills
- Background in building CI/CD pipelines for AI, monitoring model performance, and adhering to security & compliance policies
- Familiarity with AI governance frameworks, model‑monitoring platforms, and enterprise AI ethics guidelines.
- Knowledge of procurement domain concepts such as spend analytics, supplier risk scoring, and contract lifecycle management.
- History of coaching junior engineers, conducting code reviews, and fostering a collaborative development environment
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 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.
Salary Context
This $108K-$280K range is above the median for Research Engineer roles in our dataset (median: $192K across 50 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Lockheed Martin, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($194K) sits 23% below the category median. Disclosed range: $108K to $280K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Lockheed Martin AI Hiring
Lockheed Martin has 9 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Manassas, VA, US, Remote, US, Mount Laurel, NJ. Compensation range: $188K - $423K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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