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Caterpillar

Makes heavy machinery, engines and gas turbines

Principal Software Engineer - Multiple Teams

Backend EngineeringConfirmed live in the last 24 hours
Full-timeIrving, TX, USA

About the Job

Requirements

  • Extensive knowledge of decision-making processes and associated tools and techniques, with the ability to analyze situations and make informed decisions.
  • Superior understanding of effective communication concepts, tools, and techniques, with the ability to accurately transmit, receive, and interpret information and needs.
  • Expert knowledge of software development tools and activities, with the ability to produce software products or systems in line with product requirements.
  • Expert knowledge of the software development life cycle and the ability to use a structured methodology to deliver and manage new or enhanced software products.
  • Extensive knowledge of software integration processes and functions, with the ability to design, develop, and maintain interfaces and connections to alternative platforms and software packages.
  • Extensive knowledge of software product design and the ability to convert business requirements into software product designs.
  • Extensive knowledge of technical aspects of software products and the ability to design, configure, and integrate them.
  • Extensive knowledge of software product testing and the ability to design, plan, and execute testing strategies to ensure software quality and compliance with requirements.
  • 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.
  • Expertise in computer vision, sensor-based systems, or machine-learning-driven products.
  • Strong experience designing annotation platforms, labeling systems, or data quality frameworks.
  • Strong understanding of data-centric artificial intelligence 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 Amazon Web Services cloud services and architecture patterns; Azure or Google Cloud Platform experience is also valued.
  • Experience leading technical strategy across multiple engineering teams in agile environments.
  • Demonstrated ability to work effectively in 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.

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 metrics for consistency, agreement, completeness, and fitness for model use.
  • Establish standards for dataset readiness and production acceptance for machine-learning systems.
  • Design approaches for continuous data quality monitoring and improvement.
  • Establish standards for auditability and dataset governance.
  • Define how model performance informs 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.
  • Provide design direction to annotation and dataset quality engineering teams.
  • Review and guide detailed designs to align with system-wide architecture.
  • Partner with machine-learning, 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 development efforts using Python and/or Java in enterprise-scale environments.
  • Champion software development life cycle discipline, including continuous integration and continuous delivery, automated testing, infrastructure as code, code quality, release management, and operational maturity.
  • Establish engineering practices that support 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 discipline.
  • 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 with architectural strategy.
  • Facilitate technical design reviews, sprint planning, backlog refinement, dependency management, and engineering governance activities.
  • Promote collaboration, accountability, adaptability, and engineering rigor in the engineering culture.

Desired Qualifications

  • Experience supporting artificial intelligence/machine-learning, autonomous systems, computer vision, robotics, or advanced analytics platforms.
  • Experience with annotation strategies for multimodal data such as LiDAR, image, and telemetry data.
  • Familiarity with annotation tools or platforms such as CVAT, Labelbox, or SuperAnnotate.
  • Experience with auto-labeling, active learning, or hybrid annotation systems.
  • Experience leading or influencing annotation operations at scale, internally or through vendors.

About the Company

Caterpillar

Makes heavy machinery, engines and gas turbines

Caterpillar makes construction and mining machines, diesel and natural gas engines, industrial gas turbines and locomotives for contractors, mines, energy companies and data center builders. It is the world's largest maker of construction and mining equipment, and its yellow Cat bulldozers and excavators work in nearly every country. Customers buy or rent machines through independent Cat dealers, while its engines and turbines generate electricity and push natural gas through pipelines. Caterpillar earns money from equipment sales and from the parts and repairs that dealers supply over a machine's working life. It was formed in 1925 when California's Holt and Best tractor companies merged, and it is based in Irving, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Irving, TX
Founded
1925

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