C

Caterpillar

Makes heavy machinery, engines and gas turbines

AI Development Engineer

Applied Machine LearningUpdated on Nov 9
Full-timeNo salary listedIn-person

About the Job

Requirements

  • Bachelor's degree or higher in computer science, software engineering, artificial intelligence, automation, or a related major.
  • At least 5 years of experience developing software platforms, autonomous-driving data platforms, or AI engineering systems.
  • Proficiency in Python and one of C++, Go, or Java.
  • Familiarity with web back ends, RESTful APIs, database design, Docker, Linux, and CI/CD.
  • Practical experience with VLMs or multimodal large language models, including independently completing development from solution design through deployment.
  • Familiarity with fine-tuning methods including SFT, LoRA, and QLoRA, and ability to build multimodal training and evaluation datasets.
  • Familiarity with prompt engineering, RAG, vector retrieval, tool calling, or agent workflows.
  • Experience deploying and optimizing VLM inference, including quantization, GPU memory optimization, or inference acceleration.
  • Familiarity with React, Vue, or TypeScript and ability to deliver complex data visualizations.
  • Familiarity with databases, object storage, caching, and message queues.
  • Familiarity with machine-learning or deep-learning model training, evaluation, and deployment processes.

Responsibilities

  • Develop vehicle-side fault-event detection and automatic data-slicing and return tools, supporting fault-signal triggers from perception, localization, planning, and control nodes.
  • Build an enterprise autonomous-driving data-closed-loop platform to manage returned data across multiple mines, vehicles, and software versions, and connect it with the data-annotation platform.
  • Develop VLM-based automated issue diagnosis by organizing images, point clouds, trajectories, logs, and fault codes as model context to produce fault descriptions, root-cause analyses, and troubleshooting recommendations.
  • Use RAG, tool calling, or agent workflows for complex diagnosis, and establish evidence citations and human-confirmation mechanisms to control model hallucinations.
  • Build VLM domain fine-tuning and evaluation systems using SFT, LoRA, and QLoRA, and continuously incorporate field data for closed-loop iteration.
  • Build an MLOps pipeline supporting one-click training, validation, export to ONNX or TensorRT, and vehicle-side deployment for perception and fusion models.
  • Design the platform architecture and develop core components to ensure scalability, high availability, and observability.

Desired Qualifications

  • Experience with autonomous driving, robotics, ADAS, or mining automation projects.
  • Experience with autonomous-driving fault diagnosis, data replay, or corner-case mining.
  • Familiarity with ROS or ROS2, ROS Bag, MCAP, and related data formats or communication frameworks.
  • Familiarity with open-source multimodal models such as LLaVA, Qwen-VL, InternVL, or CLIP.
  • Familiarity with Kubernetes, GPU scheduling, vLLM, Triton, or tools such as MLflow or Kubeflow.

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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