Principal Architect - AI Factory (Physical AI)

Caterpillar•Irving, TX

6d•$159,120 - $258,570•Onsite

About The Position

The Principal Architect for AI Factory defines the data architecture that enables Physical AI and autonomous systems to learn, reason, and operate at scale. The role establishes data domains, canonical data models, data products, and architectural standards to turn jobsite, machine, sensor, telemetry, simulation, and operational data into trusted, reusable assets for AI development and autonomy. It provides technical leadership for organizing, managing, and delivering data across the enterprise so engineers and data scientists can efficiently develop, train, fine-tune, validate, and deploy AI models, while accelerating innovation through scalable data products. The architect works at the intersection of data, AI, digital twins, and autonomy, and develops detailed architecture deliverables to solve business problems. The role designs application technical infrastructure, evaluates and deploys technologies that enhance digital capabilities, and collaborates with cross-functional teams to address application requirements and deliver business results. The position is full-time at Caterpillar’s Irving, Texas (Dallas) office. Domestic relocation assistance and visa sponsorship are available. The salary range is $159,120 to $258,570.

Requirements

Knowledge of data architecture processes, techniques, and influencing factors; ability to design blueprints integrating data resources for business processes and functional support.
Analytical thinking skills, including the ability to determine root causes of organizational problems and develop alternative solutions.
Knowledge of platform architecture technologies and methods; ability to design roadmaps and deploy interfaces for end users in accordance with standards and processes.
Knowledge of requirements analysis tools, methods, and techniques; ability to elicit, analyze, and record functional and nonfunctional business requirements.
Knowledge of target architecture and ability to develop an IT blueprint and roadmap aligned with business strategies and objectives.
Bachelor’s degree in Computer Science, Computer Engineering, or a related field.
Ability to work full-time at the Irving, TX office.

Nice To Haves

Typically 8+ years architecting distributed AI infrastructure on-premises and in the cloud, including multi-node GPU clusters, high-speed fabrics, AI storage, and cluster sizing.
Experience with Kubernetes for GPU workloads, Slurm, Run:ai, NVIDIA Mission Control, or Ray; infrastructure as code such as Terraform, Ansible, or Helm; and hybrid cloud/on-premises environments.
Experience with distributed training and performance technologies and practices, including PyTorch FSDP, TensorRT, DeepSpeed, Megatron, NCCL, profiling, and elastic or fault-tolerant training.
Experience building data flywheels for petabyte-scale multimodal sensor data, including ingestion, lakehouse/object storage, distributed ETL, curation, labeling, dataset versioning and lineage, and synthetic data integration.
Experience with MLOps and model lifecycle tools such as MLflow, Weights & Biases, or Kubeflow, including training, fine-tuning, evaluation, deployment, monitoring, governance, and VLA/foundation-model post-training workflows.
Experience with simulation-in-the-loop and reinforcement-learning infrastructure, synthetic data pipelines, closed-loop RL, and simulation/replay-based evaluation.
Experience with NVIDIA technologies and edge deployment, including DGX, NVIDIA AI Enterprise, NeMo, NIM, Triton, TensorRT, Dynamo, CUDA, and Jetson/DRIVE-class edge compute.
Experience with observability, reliability, and security practices, including DCGM, Prometheus, Grafana, Loki, SRE practices, SLOs, GPU FinOps, and security and governance controls for safety-relevant autonomy software.
Strong Python and working C++ skills; experience with containers, CI/CD, Bazel or monorepo tooling, and AWS or Azure; sound build-versus-buy judgment.
Experience with ML infrastructure or data/compute platforms for autonomous vehicles or robotics, or AI Factory reference architectures.
Master’s or doctoral degree preferred.

Responsibilities

Define data architecture for Physical AI and autonomous systems at scale.
Establish data domains, canonical data models, data products, and architectural standards.
Transform jobsite, machine, sensor, telemetry, simulation, and operational data into trusted, reusable assets for AI development and autonomy.
Provide technical leadership for organizing, managing, and delivering data across the enterprise.
Enable engineers and data scientists to develop, train, fine-tune, validate, and deploy AI models efficiently.
Develop detailed architecture deliverables to solve business problems.
Design application technical infrastructure, including databases, programming languages, utilities, and testing approaches.
Lead evaluation and deployment of technologies that add to or enhance existing digital technical capabilities.
Address application business requirements and collaborate with cross-functional teams to deliver digital solutions and business results.
Translate architecture into implementation plans, reference architectures, and standards across platform teams.
Partner with Cat Digital Division and NVIDIA or cloud providers on enterprise platform standards.