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About This Role
Career Area:
Engineering
Job Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
Senior Gas Turbine Performance Engineer: AI Accelerated Engineering
Why this role
If you love the rigor of thermodynamics and the satisfaction of making models match reality, and you’re excited about using modern AI tools to amplify engineering impact, this role is built for you. You’ll sit at the crossroads of engine physics, product decisions, and real operating data, building performance models and software that influence everything from new product definition to test cell execution to fielded fleet operations.
What the Performance Group does
The Gas Turbine Performance Group leads concept and advanced cycle studies and delivers calibrated performance predictions for new and upgraded engines. We oversee performance during engine development, support production and acceptance testing, and enable product support teams worldwide with the tools needed to analyze, predict, and guarantee engine performance.
A core part of our mission is building and maintaining the ecosystem (models, workflows, automation, and data pipelines) that turns thermodynamics, test, and field data into decisions. Increasingly, our transient cycle models and calibrated physics tools are the foundation for AI enabled digital twins, unlocking longer service intervals, better cycle count capability, and improved efficiency through operational flexibility.
What you will do
Turn data into engineering decisions.
Analyze acceptance test and fleet/field datasets to trend performance, troubleshoot issues, and monitor aeromechanical/operability behavior across product lines.
Own high\-stakes performance predictions.
Build, calibrate, and defend steady state and transient performance models for new products, upgrades, and investigations, where accuracy matters and assumptions must be explicit.
Build the tools engineers rely on.
Develop and maintain the internal software used across design, sales/applications, customer services, overhaul, and customer sites, improving usability, automation, traceability, and confidence in results.
Accelerate engineering with AI.
Apply modern AI techniques:
- speeding up calibration and uncertainty exploration,
- enabling anomaly detection and diagnostics,
- creating reduced‑order models that stay grounded in physics,
- supporting digital twin workflows using physics‑guided learning approaches.
Collaborate across disciplines.
Work shoulder to shoulder with design, controls, test, operations, and digital teams to solve urgent problems quickly while also building long term capability.
What you’ll love about the work
- Physics first engineering with direct line of sight to real hardware and customer outcomes.
- High leverage: your models and tools scale across programs, teams, and global sites.
- A modern toolchain mindset: automation, reproducibility, and AI assisted workflows are part of the job, not a hobby.
What you will bring (required)
- B.S. / B.Eng. in Mechanical or Aerospace Engineering (or closely related field) plus ex perience with multiple design cycles within industry and performance engineering to real world problems.
- Foundation of knowledge for growth in gas turbine performance and thermodynamics (cycles, maps, matching, operability concepts, off\-design behavior).
- Ability to work through ambiguity, manage priorities , and deliver high\-quality results under schedule pressure.
- Clear written and verbal communication . You can explain assumptions, results, and risks to both technical and nontechnical partners.
- Proven habit of finishing work with thoroughness and reliability (validation, documentation, and follow\-through).
What sets top candidates apart (preferred)
- Applicable experience in gas turbine performance, aerodynamics, instrumentation, real world testing or closely related area.
- Applied knowledge of model calibration and troubleshooting of gas turbine performance/operability in steady state and/or transient.
- Strong engineering software skills, building scripts, code, or automation that makes other engineers faster and improves quality.
- Familiarity with controls and control logic and controls software .
Tools \& technologies (typical, not exhaustive)
- 1‑D multiphysics engine performance tools (e.g., NPSS, PROOSIS, GasTurb, pyCycle or similar)
- Programming/scripting: Python (strongly preferred), and/or C/C\+\+/C\#/Fortran/VBA
- Engineering computing: MATLAB / Simulink
- Analytics \& visualization: Tableau (or similar), plus modern data tooling as applicable
Standard productivity tools (Microsoft Office suite)
*
Summary Pay Range:
$112,704\.00 \- $169,056\.00
Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job\-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
- Medical, dental, and vision benefits\*
- Paid time off plan (Vacation, Holidays, Volunteer, etc.)\*
- 401(k) savings plans\*
- Health Savings Account (HSA)\*
- Flexible Spending Accounts (FSAs)\*
- Health Lifestyle Programs\*
- Employee Assistance Program\*
- Voluntary Benefits and Employee Discounts\*
- Career Development\*
- Incentive bonus\*
- Disability benefits
- Life Insurance
- Parental leave
- Adoption benefits
- Tuition Reimbursement
- These benefits also apply to part\-time employees
This position requires working onsite five days a week.
Relocation is available for this position.
Visa Sponsorship is not available for this position.
Posting Dates:
July 21, 2026 \- August 21, 2026
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community .
Salary Context
This $112K-$169K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Solar Turbines, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $112K to $169K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Solar Turbines AI Hiring
Solar Turbines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $169K - $169K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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