Principal Software Engineer – Dataset and Annotation Quality - Physical AI, Autonomy & Data Platform

Caterpillar•Irving, TX

11d

About The Position

Caterpillar is seeking a highly experienced Principal Software Engineer to define and lead the system design of annotation and dataset quality systems that support machine learning and AI for physical systems and autonomy. The role designs how raw sensor data becomes high-quality labeled datasets, ensuring training data is accurate, consistent, and aligned with model needs. The engineer serves as the technical authority for annotation and dataset quality, establishing system designs, standards, and engineering patterns for teams to implement and scale. This is a hands-on technical role addressing labeling consistency, ontology design, quality validation, human-in-the-loop workflows, and continuous dataset improvement. It requires expertise in data-centric AI, annotation systems, and quality engineering, as well as the ability to work in ambiguous environments where requirements evolve with model performance and real-world behavior. The position is based in Irving, Texas (Dallas), and domestic relocation assistance is available. Visa sponsorship is available. The position works at the intersection of physical systems and software to help build intelligent systems for construction autonomy. Caterpillar's technology connects machines, data, and people, using data from more than 1.6 million connected assets to support AI models, edge computing architectures, and software systems across fleets, products, and industries.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field (listed among top candidate qualifications).
10+ years of software engineering experience, including significant experience in principal-, staff-, or lead-level technical leadership roles.
Expert-level proficiency in Python and/or Java.
Expert knowledge of software development tools and activities, with the ability to deliver software products or systems to product requirements.
Expert knowledge of the software development life cycle and ability to use a structured methodology to deliver and manage software products.
Extensive knowledge of software integration processes and functions, including designing, developing, and maintaining interfaces and links to other platforms and software packages.
Extensive knowledge of software product design and ability to translate business requirements into product designs.
Extensive technical knowledge of software products and ability to design, configure, and integrate their technical aspects.
Extensive knowledge of software product testing and ability to design, plan, and execute testing strategies to ensure quality and adherence to requirements.
Expertise in computer vision, sensor-based systems, or ML-driven products.
Strong experience designing annotation platforms, labeling systems, or data quality frameworks.
Strong understanding of data-centric AI and training data lifecycle management.
Experience defining label ontologies, annotation guidelines, and quality standards.
Experience building or scaling human-in-the-loop systems.
Strong understanding of data quality metrics and their impact on model performance.
Experience connecting model failures to data issues and driving improvements.
Proven ability to lead architectural direction across multiple teams.
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; Azure or GCP experience is also valued.
Experience leading technical strategy across multiple engineering teams in agile environments.
Demonstrated ability to operate effectively in highly ambiguous or rapidly evolving technical environments.
Comfort with experimentation, prototyping, and iterative architecture refinement.
Ability to make sound technical decisions with incomplete information and evolving constraints.
Extensive knowledge of decision-making processes and techniques, with the ability to analyze situations and make informed, productive decisions.
Superior understanding of effective communication and ability to transmit, receive, and accurately interpret ideas, information, and needs.

Nice To Haves

Experience supporting AI/ML, autonomous systems, computer vision, robotics, or advanced analytics platforms.
Experience with annotation strategies for multimodal data, including LiDAR, images, and telemetry.
Familiarity with annotation tools or platforms such as CVAT, Labelbox, SuperAnnotate, or similar.
Experience with auto-labeling, active learning, or hybrid annotation systems.
Experience leading or influencing annotation operations at scale, internally or through vendors.
Experience with Azure or GCP cloud services and architecture patterns.

Responsibilities

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 data quality metrics, including consistency, agreement, completeness, and fitness for model use.
Establish standards for dataset readiness, production acceptance, auditability, and dataset governance for ML systems.
Design approaches for continuous data quality monitoring and improvement.
Define how model performance informs annotation prioritization and strategy.
Design systems for active learning, error mining, and targeted data improvement.
Establish mechanisms to improve datasets continuously based on real-world model behavior.
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.
Serve as the technical authority for annotation and dataset quality across the organization.
Lead software development using Python and/or Java in enterprise-scale environments.
Champion SDLC practices, 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.
Work 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 with architectural strategy.
Facilitate technical design reviews, sprint planning, backlog refinement, dependency management, and engineering governance.
Promote collaboration, accountability, adaptability, and engineering rigor.