Lead AI Automation Engineer, Data Annotation Services

Caterpillar

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Irving, TX
Salary
$128,470–$208,770 / yr
Posted
6 days ago
Freshness
Confirmed live 2 days ago
Closes
Oct 15, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $181k
This role $169k
$117k most similar roles pay here $231k

This role pays less than 52% of similar roles. Most pay $143,000–$219,275 — the shaded band above. At the midpoint, this role pays about $169k versus about $181k for comparable roles.

Based on 240 similar postings.

Employer

About Caterpillar

Caterpillar Inc. is the world''s largest manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives. Industry: Heavy Equipment & Manufacturing

Caterpillar currently has 30 open roles on FindRole.

Listed pay typically runs $147,760–$190,176 across 29 roles with salary data.

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At a glance

TL;DR · Lead AI Automation Engineer, Data Annotation Services

Lead AI Automation Engineer, Data Annotation Services provides technical leadership for a scrum team focused on developing annotation automation solutions for robotics, autonomy, computer vision, and Vision-Language-Action systems. The role involves designing and deploying automated labeling and captioning capabilities for heavy equipment and construction environments while building data pipelines that integrate machine learning models with human-in-the-loop workflows. Key responsibilities include training and fine-tuning AI models, ensuring quality assurance, and implementing technologies like NVIDIA Cosmos Curator and Cosmos Reason. Candidates will utilize Python, PyTorch, TensorFlow, ONNX, and tools such as Labelbox or Supervisely to manage camera and LiDAR sensor datasets. The work addresses the complex challenge of construction autonomy by creating a data foundation for machines to perceive environments and make informed decisions in dynamic jobsites using multimodal AI and physical systems.

What you'll do

  • Lead technical execution for a scrum team focused on annotation automation and AI-driven data production.
  • Design and deploy automated labeling and captioning solutions for perception and Vision-Language-Action (VLA) datasets.
  • Train, fine-tune, and evaluate AI models to support auto-labeling and quality assurance.
  • Build data pipelines that move datasets through automated labeling, human review, and validation.
  • Implement and extend platforms such as NVIDIA Cosmos Curator and other emerging AI automation technologies.
  • Develop solutions integrating Caterpillar data systems with external providers and human-in-the-loop workflows.
  • Ensure reliability, scalability, and operational excellence across all annotation automation services.
  • Mentor engineers and provide day-to-day technical leadership for the team.

What we're looking for

  • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Robotics, or a related field.
  • Experience leading technical delivery for software or AI engineering teams.
  • Experience developing production-grade software solutions using Python and modern software engineering practices.
  • Experience designing and implementing distributed systems, APIs, and data processing pipelines.
  • Experience training, fine-tuning, or deploying machine learning models.
  • 3+ years of experience generating and managing camera and LiDAR sensor datasets for perception or machine learning applications.
  • 3+ years of experience in data annotation and quality assurance for autonomous systems or computer vision projects.
  • Experience using annotation tools (e.g., Labelbox, Supervisely) and ML frameworks (e.g., PyTorch, TensorFlow).
  • Experience with computer vision, robotics, autonomy, Physical AI, or multimodal AI systems (preferred).
  • Experience with AI-assisted annotation, auto-labeling, or machine learning data pipelines (preferred).
  • Experience training or fine-tuning foundation models, vision models, or multimodal models (preferred).
  • Experience with NVIDIA Cosmos Curator, Cosmos Reason, or similar AI automation platforms (preferred).
  • Experience with Vision-Language-Action (VLA) datasets and workflows (preferred).
  • Experience with sensor data such as cameras, LiDAR, radar, or autonomous machine datasets (preferred).
  • Experience with cloud platforms, MLOps, and machine learning deployment pipelines (preferred).
  • Experience building human-in-the-loop AI workflows and quality automation systems (preferred).

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