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About This Role
Career Area:
Technology, Digital and DataJob 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.
Help Build the Future of Caterpillar –
At Caterpillar, technology always has a purpose, which is to solve our customers’ toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world—on real jobsites, at global scale.
You’ll build and deploy against one of the most unique data foundations—over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale.
Be Part of What’s Next in Autonomous Construction Sites
Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible—intelligent machines operating in dynamic jobsites—into reality.
Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against.
If this work motivates you, we invite you to join our team. In these roles, you’ll work at the intersection of the physical and digital worlds. You’ll help design and deliver intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations.
Apply today to build the new era of construction autonomy at Caterpillar.
Job Summary:
The Principal Architect for AI Factory is responsible for defining the data architecture that enables Physical AI and autonomous systems to learn, reason, and operate at scale. This role establishes the data domains, canonical data models, data products, and architectural standards that transform vast amounts of jobsite, machine, sensor, telemetry, simulation, and operational data into trusted, reusable assets for AI development and autonomy. Working at the intersection of data, AI, digital twins, and autonomy, the architect provides technical leadership for how data is organized, managed, and delivered across the enterprise, ensuring engineers and data scientists can efficiently develop, train, fine-tune, validate, and deploy next-generation AI models while accelerating innovation through scalable and reusable data products.
Bottom line, you are creating the data foundation that enables AI innovation at scale.
What You Will Do:
- Developing detailed architecture deliverables to solve business problems.
- Designing an application's technical infrastructure, such as specific databases, programming languages, utilities, and testing approaches.
- Leading the evaluation and deployment of new technologies to add or enhance existing digital technical capabilities.
- Participating in addressing business requirements for applications and collaborating with cross-functional teams to deliver digital solutions that meets business results.
What You Will Have:
- Data Architecture: Knowledge of processes, techniques and factors that affect data architecture; ability to design blueprints on how to integrate data resources for business processes and functional support.
- Analytical Thinking: Knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
- Platform Architecture: Knowledge of technologies and methods to design processing mechanisms and roadmaps to execute business application systems; ability to design these roadmaps and deploy supportive interfaces for end-users to access related systems, in accordance with standards and processes.
- Requirements Analysis: Knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project.
- Target Architecture: Knowledge of target architecture; ability to develop the IT blueprint and roadmap while aligning the architecture and processes with business strategies and objectives roadmap while aligning the architecture and processes with business strategies and objectives
Top Candidates Will Have:
- AI Factory and GPU Infrastructure: Typically 8+ years architecting distributed AI infrastructure on-premises and in the cloud: multi-node GPU clusters (DGX / HGX / GB200-class or cloud equivalents), NVLink / InfiniBand / Spectrum-X fabrics, high-throughput AI storage, and cluster sizing.
- Workload Orchestration – Deep Kubernetes for GPU workloads (operators, multi-tenant queues, quotas) plus Slurm, Run:ai / NVIDIA Mission Control, or Ray; infrastructure as code (Terraform, Ansible, Helm); hybrid cloud / on-prem.
- Distributed Training and Performance – PyTorch FSDP / TensorRT/ DeepSpeed / Megatron, NCCL collectives, parallelism strategies, profiling (Nsight), elastic and fault-tolerant training; drives goodput and cost per unit of data.
- Data Flywheel for Physical AI – Petabyte-scale multimodal sensor data: ingestion, lakehouse / object storage, distributed ETL (Spark, Ray Data, Airflow, Flyte), curation, labeling and auto-labeling, dataset versioning and lineage, synthetic data integration.
- MLOps and Model Lifecycle – Training, fine-tuning, evaluation, registry, deployment, monitoring, and governance with MLflow, Weights & Biases, Kubeflow, or similar; VLA / foundation-model post-training workflows.
- Simulation-in-the-Loop and RL Infrastructure – Synthetic data pipelines (Omniverse Replicator, Isaac Sim, NVIDIA Cosmos), large-scale closed-loop RL infrastructure (Isaac Lab, Ray RLlib), and simulation / replay-based evaluation orchestrating millions of jobs.
- NVIDIA Ecosystem and Edge Deployment – DGX, NVIDIA AI Enterprise, NeMo, NIM, Triton, TensorRT, Dynamo, CUDA, and Jetson / DRIVE-class edge compute; path from trained model to optimized, monitored inference on machines.
- Observability, Reliability, and Security – DCGM / Prometheus / Grafana / Loki, SRE practices and SLOs, FinOps for GPU cost, and security and governance controls for safety-relevant autonomy software.
- Software Engineering – Strong Python and working C++; containers, CI/CD, Bazel or monorepo tooling, cloud (AWS / Azure); sound build-versus-buy judgment.
- Architecture Leadership – Translates architecture into implementation plans, reference architectures, and standards across platform teams; partners with Cat Digital Division and NVIDIA / cloud providers on enterprise platform standards.
- Domain Exposure – ML infrastructure or data / compute platforms for AV or robotics (Applied Intuition, Waabi, Wayve, Waymo, Zoox, Tesla, NVIDIA, Aurora), or AI Factory reference architectures (NVIDIA, Dell, HPE, Siemens).
- Bachelor’s degree in Computer Science, Computer Engineering, or related field; master’s or doctoral degree preferred.
Additional Details:
- This position requires the candidate to work full-time at the Irving, TX office (Dallas)
- Domestic relocation assistance is available for this position.
- Visa sponsorship is available with this position.
Why Caterpillar
You'll work on some of the most challenging applications of AI in the physical world, helping build systems that transform vast amounts of machine and sensor data into the intelligence that powers autonomous and AI-enabled equipment. If you're excited about applying AI to real-world machines operating in complex environments, we'd like to hear from you.
Summary Pay Range:
$159,120.00 - $258,570.00 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
Posting Dates:
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 $159K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Caterpillar, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $159K to $258K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Caterpillar AI Hiring
Caterpillar has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Software Engineer. Positions span Irving, TX, US, Chicago, IL, US, Cary, NC, US. Compensation range: $158K - $258K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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