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About This Role
Job Description Summary
Transform engine and spare-part delivery for Defense Engines & Services (DES). This role owns the product vision, roadmap, and end-to-end lifecycle for the data, analytics, and artificial intelligence (AI) capability that makes that transformation possible. Act as the customer representative for a portfolio of interdependent products spanning new engine production and Maintenance, Repair & Overhaul (MRO), from demand and forecast through fulfillment and service and support. Success is measured through DES KPIs, led by on-time delivery and spare-part output, then inventory and net productivity.
This is a forward-deployed role. You will work alongside the planners, operators, and engineers who own the delivery, and you will build digital capability with them, taking AI-enabled ideas from concept to medium and high-fidelity working prototypes on real production data.Job Description
This role will also require in-person attendance for New Hire Orientation on Day 1
Roles and Responsibilities
In this role, you will:
- Own all aspects of the lifecycle (planning, forecasting, production) for multiple interdependent data, analytics, and AI products that together form the DES delivery transformation program and represent the customer in every scope and sequencing tradeoff.
- Build and maintain the DES data and analytics product roadmap, sequence it against the constraints that actually limit engine and spare-part output, and identify and secure the stakeholders needed to support it.
- Stand up self-service data and analytics on manufacturing, quality, and delivery data so planners, buyers, and shop leaders answer their own questions in the moment rather than opening a DT ticket. This closes the single largest capability gap in the portfolio.
- Operationalize and sustain value from advanced analytics and agentic AI investments, and move proven prototypes inside the operating model rather than leaving them adjacent to it, where they stay advisory.
- Partner with central DT and Enterprise Data on data fabric and ontology adoption so DES manufacturing, quality, and delivery data is modeled once and reused across products.
- Take AI-enabled ideas from concept to working software. Build low-fidelity proofs to test feasibility, then advance the survivors to medium and high-fidelity prototypes on real production data, in the hands of real users, and decide quickly which ones earn production investment and which get killed.
- Work forward-deployed. Embed with operators, planners, and engineers at the Genba, build with them rather than collecting requirements and handing them off, and stay on the problem until the capability is in use.
- Use rapid prototyping as a countermeasure inside Daily Management, converting Key Performance Indicator (KPI) and Targets to Improve (TTI) gaps into working capability in weeks rather than quarters.
- Define the value case in delivery terms up front (engine days, turnaround time, schedule adherence, inventory turns, cost), drive early materialization of benefits, and report outcomes against the 2026 DES measures. Stop or redirect work that does not move a delivery number.
- Influence Executive Band (EB) leaders and below on investment decisions, and partner with business, finance, and DT leadership to align Digital Technology spend with value stream priorities.
- Set data and AI standards across the DES horizontal so use, implementation, support, and technical integrity stay consistent and do not fragment by product line.
- Operate as a player-coach. Mentor product managers and engineers on systems thinking for large-scale problems, lean and experimental product practice, and raise the team's data and AI fluency.
- Manage supplier and contractor partners delivering analytics and AI work, hold them to outcomes, and reduce the team's dependence on expensive external delivery.
- Lead risk management for the portfolio, including export control, cybersecurity, data governance, and responsible AI, and design controls into products instead of relying on exception processes.
Business Acumen
- Translates business problems into product bets, quantifies the value, and makes tradeoffs across a multi-product portfolio based on business priority rather than the loudest request.
- Establishes performance metrics and drives continuous improvement across systems, processes, and resources.
- Demonstrated customer focus; evaluates decisions through the eyes of the operator on the floor and partners with the business to shape future initiatives.
- Decomposes problems and decides when the problem or the solution is not fully defined.
- Constructs multiyear scenarios for data-centric methods and technologies under uncertainty, and interprets industry trends that should inform the roadmap.
Leadership
- Sets and changes direction at the enterprise level by building direct and indirect support for ideas, largely without formal authority.
- Leads and influences across a complex matrix organization, including Executive Band leaders and their teams, and incorporates stakeholder input while still deciding.
- Directs and mentors others toward a systems thinking approach for solving large-scale problems, and removes roadblocks on behalf of the team.
- Owns decisions, including outcomes and any required rework plans.
- Acts as a catalyst for change, rewards early adopters, and builds a culture of proactive problem solving rather than reactive break-fix.
Personal Attributes
- Excellent written and oral communication; presents concise facts and the implications of alternatives in a way that is relevant to senior leadership and customers.
- Strategic thinker who converts vision into a concrete action plan with named owners and dates.
- Manages uncertainty for self and others, and makes confident decisions when problems or solutions are not 100% defined.
- Curious and creative; removes barriers to the creativity of others and pushes simplicity and speed.
- Biased toward demonstration over documentation. Would rather show a working prototype than present a plan for one.
- Strong analytical, project management, and organizational skills, with the ability to prioritize under pressure and tight deadlines.
Required Qualification
Bachelor’s degree from accredited university or college with minimum of 10 years of professional experience OR Associates degree with minimum of 13 years of professional experience OR High School Diploma with minimum of 15 years of professional experience
Minimum 8 years of professional experience in IT
Note: Military experience is equivalent to professional experience
Ability to travel; 10-50%
Eligibility Requirement:
- Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job.
Desired Qualification
Technical Expertise
- Hands-on builder with a forward-deployed engineering mindset. Personally produces working artifacts (data pipelines, ontology and semantic models, notebooks, agent workflows, and functional user interfaces) rather than only specifying them, and is fluent in Python, SQL, or equivalent.
- Demonstrated ability to move an idea across fidelity levels under time pressure: proof of concept to prove feasibility, medium fidelity to prove the workflow, high fidelity to prove production readiness and earn investment.
- Demonstrated ownership of data platform and advanced analytics products at scale, covering ingestion, modeling, governance, performance, and self-service consumption.
- Working fluency with modern analytics and AI, including machine learning, large language model, retrieval, and agentic patterns, and the judgment to know where those methods do not belong.
- Working knowledge of manufacturing and MRO data and systems, including Manufacturing Execution System (MES), Enterprise Resource Planning (ERP), quality, and shop-floor technology, and how that data becomes a delivery or inventory decision.
- Deep technology, process, and domain expertise sufficient to influence product design whether the product is built internally or bought.
- Strong track record of understanding current and emerging technologies, demonstrated through training, job experience, or industry activities.
- Demonstrated understanding of Lean Six Sigma principles (for example, Green Belt certified) and comfort operating inside a Daily Management system.
The base pay range for this position is $176,000 - $235,000 annually. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. This position is also eligible for an annual discretionary bonus based on a percentage of your base salary/ commission based on the plan. This posting is expected to close on November 6th, 2026.
GE Aerospace offers comprehensive benefits and programs to support your health and, along with programs like HealthAhead, your physical, emotional, financial and social wellbeing. Healthcare benefits include medical, dental, vision, and prescription drug coverage; access to a Health Coach from GE Aerospace; and the Employee Assistance Program, which provides 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Aerospace Retirement Savings Plan, a 401(k) savings plan with company matching contributions and company retirement contributions, as well as access to Fidelity resources and planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability insurance, life insurance, and paid time-off for vacation or illness.
GE Aerospace (General Electric Company or the Company) and its affiliates each sponsor certain employee benefit plans or programs (i.e., is a “Sponsor”). Each Sponsor reserves the right to terminate, amend, suspend, replace or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a Sponsor’s welfare benefit plan or program. This document does not create a contract of employment with any individual.
This role requires access to U.S. export-controlled information. Therefore, employment will be contingent upon the ability to prove that you meet the status of a U.S. Person as one of the following: U.S. lawful permanent resident, U.S. Citizen, have been granted asylee or refugee status (i.e., a protected individual under the Immigration and Naturalization Act, 8 U.S.C. 1324b(a)(3)).
Additional Information
GE Aerospace offers a great work environment, professional development, challenging careers, and competitive compensation. GE Aerospace is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE Aerospace will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Employees may also be subject to random and reasonable-suspicion drug and alcohol testing.
Relocation Assistance Provided: No
Salary Context
This $176K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At GE Aerospace, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $176K to $235K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
GE Aerospace AI Hiring
GE Aerospace has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Livonia, MI, US, Remote, US, Evendale, OH, US. Compensation range: $127K - $235K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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