Principal Software Engineer – Dataset and Annotation Quality - Physical AI, Autonomy & Data Platform
Caterpillar Inc. · Irving, Texas, United States
Career Area:
Technology, Digital and Data
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.
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:
We are seeking a highly experienced Principal Software Engineer to define and lead the system design of annotation and dataset quality systems that enable machine learning and AI for physical systems for autonomy.
This role is responsible for designing the end-to-end system design for how raw sensor data is transformed into high-quality labeled datasets, ensuring that training data is accurate, consistent, and aligned to model needs.
The Principal Software Engineer will act as the technical authority for annotation and dataset quality, defining system design, standards, and engineering patterns that annotation and dataset quality engineering teams will implement and scale.
This is a hands-on technical role, focused on solving complex challenges such as labeling consistency, ontology design, quality validation, human-in-the-loop workflows, and continuous dataset improvement.
Success in this role requires expertise in data-centric AI, annotation systems, and quality engineering, along with the ability to operate in ambiguous environments where requirements evolve based on model performance and real-world behavior.
What You Will Do:
Data Quality Architecture & Standards
- Define and establish data quality frameworks, including validation rules, scoring models, and acceptance criteria.
- Design systems for label validation, consistency checking, and anomaly detection at scale.
- Define metrics that matter (e.g., consistency, agreement, completeness, fitness for model use).
- Establish standards for dataset readiness and production acceptance for ML systems.
- Design approaches for continuous data quality monitoring and improvement.
- Establish standards for auditability and dataset governance.
Quality Feedback Loops (Data-Centric AI)
- Define how model performance feeds back into annotation prioritization and strategy.
- Design systems for active learning, error mining, and targeted data improvement.
- Establish mechanisms to continuously improve datasets based on real-world model behavior.
Technical Leadership & Influence
- Provide design direction to annotation and dataset quality engineering teams.
- Review and guide detailed designs to ensure alignment with system-wide architecture.
- Partner with ML, autonomy, and product teams to ensure data quality drives system performance.
- Act as the technical authority for annotation and dataset quality across the organization.
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, Annotation 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 Eng