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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.
We are seeking a highly experienced Principal Software Engineer to lead the technical strategy and engineering execution for large\-scale data ingestion and processing platforms supporting physical AI and autonomous systems.
This role is responsible for driving engineering excellence across distributed scrum teams while designing scalable, cloud\-native solutions for ingesting and processing high\-volume sensor and telematics data including LiDAR, radar, video, image, and vehicle telemetry streams. The Principal Software Engineer will partner closely with Principal Data Architects, Product Owners, and Engineering Leadership to define reusable data platform capabilities that support advanced analytics, machine learning, and autonomy initiatives.
This is a hands\-on technical leadership role with significant influence on platform architecture, engineering standards, scalability strategy, and long\-term technology direction. The ideal candidate combines deep expertise in software engineering, distributed systems, cloud architecture, and SDLC discipline with the ability to lead engineering efforts in highly ambiguous and rapidly evolving technical domains.
This role operates at the frontier of physical AI and autonomy engineering, where technologies, architectural patterns, and best practices are continuously evolving. Success in this position requires an engineer who thrives in ambiguity, adapts quickly to emerging technologies, and can drive progress despite incomplete or constantly changing information.
The Principal Software Engineer will serve as a technical anchor and role model for software and data engineering teams, helping establish a culture that embraces experimentation, iterative development, and continuous learning. This individual must be highly effective operating in “the grey” — balancing strategic architectural thinking with pragmatic execution in a frontier engineering environment.
What You Will Do:
Frontier Engineering \& Innovation Leadership
- Lead engineering efforts in emerging domains related to physical AI, autonomy, and next\-generation sensor\-driven systems.
- Operate effectively in environments with evolving requirements, incomplete datasets, and rapidly changing technology landscapes.
- Drive iterative development practices that enable rapid experimentation, feedback loops, and continuous platform evolution.
- Guide engineering teams through technical uncertainty by decomposing ambiguous problems into actionable engineering strategies.
- Foster a culture of innovation, adaptability, resilience, and continuous learning across engineering organizations.
- Evaluate emerging technologies, frameworks, and architectural approaches to support long\-term platform evolution.
- Partner with architects, researchers, and product leaders to translate innovative concepts into scalable production systems.
- Establish engineering patterns that support agility while maintaining scalability, reliability, and long\-term maintainability.
Data Platform \& Pipeline Engineering
- Design and oversee implementation of scalable ingestion pipelines for LiDAR, radar, video, image, and telematics data.
- Partner with Principal Data Architects to design reusable data products and domain\-oriented data models.
- Architect and optimize Bronze, Silver, and Gold data layer pipelines supporting both streaming and batch processing workloads.
- Ensure data pipelines are performant, fault tolerant, observable, secure, and cost optimized.
- Drive metadata, lineage, governance, and reusable data object standards across the platform.
- Enable downstream analytics, AI/ML, computer vision, and operational use cases through robust data engineering practices.
- Design reusable ingestion and transformation frameworks capable of supporting rapidly evolving autonomy workloads.
Cloud \& Distributed Systems Engineering
- Design and implement highly scalable solutions on AWS or comparable cloud platforms such as Azure or GCP.
- Lead adoption of cloud\-native architectures including microservices, event\-driven systems, and distributed processing frameworks.
- Architect real\-time and near\-real\-time streaming solutions using technologies such as Kafka, Kinesis, Spark Streaming, Flink, or equivalent.
- Design large\-scale batch processing frameworks for high\-throughput data workloads.
- Optimize infrastructure for scalability, resiliency, latency, observability, and cost efficiency.
- Drive architectural decisions supporting large\-scale distributed compute and storage systems.
Software Engineering Excellence
- Lead development efforts using Python and/or Java in enterprise\-scale environments.
- Champion SDLC discipline including CI/CD, automated testing, infrastructure as code, code quality, release management, and operational maturity.
- Establish engineering practices supporting reliability, observability, maintainability, and platform stability.
- Participate in hands\-on development, prototyping, troubleshooting, and performance tuning of critical platform components.
- Drive modernization initiatives and continuous improvement of engineering processes and platform capabilities.
- Promote iterative engineering practices that balance rapid innovation with production\-grade engineering discipline.
Agile \& Cross\-Team Collaboration
- Work closely with Product Management, Data Engineering, ML Engineering, Platform Engineering, and DevOps teams.
- Translate business and platform objectives into actionable technical roadmaps for scrum teams.
- Provide technical leadership across multiple agile teams and ensure alignment to architectural strategy.
- Facilitate technical design reviews, sprint planning, backlog refinement, dependency management, and engineering governance activities.
- Influence engineering culture by promoting collaboration, accountability, adaptability, and engineering rigor.
What You Will Have:
- Decision Making and Critical Thinking: Extensive knowledge of the decision\-making process and associated tools and techniques; ability to accurately analyze situations and reach productive decisions based on informed judgment.
- Effective Communications: Superior understanding of effective communication concepts, tools and techniques; ability to effectively transmit, receive, and accurately interpret ideas, information, and needs through the application of appropriate communication behaviors.
- Software Development: Expert knowledge of software development tools and activities; ability to produce software products or systems in line with product requirements.
- Software Development Life Cycle: Expert knowledge of software development life cycle; ability to use a structured methodology for delivering and managing new or enhanced software products to the marketplace.
- Software Integration Engineering: Extensive knowledge of software integration processes and functions; ability to design, develop and maintain interfaces and linkage to alternative platforms and software packages.
- Software Product Design/Architecture: Extensive knowledge of software product design; ability to convert business requirements into the software product design.
- Software Product Technical Knowledge: Extensive knowledge of technical aspects of a software products; ability to design, configure and integrate technical aspects of software products.
- Software Product Testing: Extensive knowledge of software product testing; ability to design, plan, and execute testing strategies and tactics to ensure software product quality and adherence to stated requirements.
Top Candidates Will Have:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or related field.
- 10\+ years of software engineering experience with significant experience in principal, staff, or lead\-level technical leadership roles.
- Expert\-level proficiency in Python and/or Java.
- Deep expertise in system design, distributed systems, and large\-scale cloud\-native architectures.
- Strong experience designing and implementing streaming and batch data processing systems at enterprise scale.
- Hands\-on experience with AWS cloud services and architecture patterns; experience with Azure or GCP also valued.
- Proven experience building scalable ingestion pipelines for high\-volume structured and unstructured data.
- Experience working with sensor\-based or telemetry data domains such as LiDAR, radar, video, imagery, IoT, or vehicle telematics.
- Strong understanding of modern data lake/lakehouse architectures and medallion (Bronze/Silver/Gold) data modeling patterns.
- Experience with technologies such as Kafka, Kinesis, Spark, Flink, Airflow, Databricks, EMR, or equivalent platforms.
- Strong understanding of CI/CD pipelines, DevOps practices, automated testing, infrastructure as code, and operational excellence.
- Experience leading technical strategy across multiple engineering teams in agile environments.
- Demonstrated success operating effectively in highly ambiguous or rapidly evolving technical environments.
- Strong comfort level with experimentation, prototyping, and iterative architecture refinement.
- Ability to make sound technical decisions with incomplete information and evolving constraints.
Additional Details:
- This position requires the candidate to be based in Irving, Texas.
- Relocation assistance is available for this position
- Visa sponsorship is available for this position
- Experience supporting AI/ML, autonomous systems, computer vision, robotics, or advanced analytics platforms.
- Familiarity with geospatial data processing and high\-throughput sensor fusion pipelines.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Knowledge of data governance, lineage, security, and compliance best practices.
AWS, Azure, or GCP cloud certifications.
- Experience implementing observability, site reliability engineering (SRE), and platform reliability practices.
- Passion for advancing technologies in physical AI, autonomy, sensor intelligence, and large\-scale data systems.
- Posting Dates: 7/21\-7/31
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
This position requires working onsite five days a week.
Relocation is available for this position.
Visa sponsorship is available for eligible applicants.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 Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Caterpillar, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($208K) sits 5% below the category median. Disclosed range: $159K to $258K.
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.
Caterpillar AI Hiring
Caterpillar has 2 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist. Positions span Irving, TX, US, Peoria, IL, US. Compensation range: $183K - $258K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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.
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