Principal Software Engineer - Simulation Data, Validation & Infrastructure

Caterpillar Inc.•Irving, TX

2d•$147,760 - $240,110•Onsite

About The Position

Caterpillar is hiring a Principal Software Engineer to lead the data, validation, and accelerated-computing foundation needed to operate simulation at enterprise scale. The role connects physical-machine evidence with virtual-machine behavior through reproducible pipelines and trusted infrastructure supporting construction autonomy and Physical AI. This hands-on technical leadership position is responsible for architecture, critical implementation, engineering standards, cross-team alignment, and mentoring. The engineer will own a major technical capability across teams and programs, translate ambiguous problems into scalable architectures and production software, and balance research and prototyping with secure, maintainable delivery. The position is based in Irving, Texas (Dallas). Domestic relocation assistance and visa sponsorship are available. The stated salary range is $147,760 to $240,110.

Requirements

Advanced software engineering experience using Python and one or more of C++, C#, Java, Go, or Node.js.
Experience with APIs, distributed systems, containers, automated testing, CI/CD, source control, and observability.
Experience with cloud platforms and cloud-native architectures, including Docker and Kubernetes.
Demonstrated ability to lead architecture and technical delivery across multiple teams.
Strong communication, mentoring, problem-solving, and cross-functional collaboration skills.
Experience using AI-assisted development tools such as GitHub Copilot, Claude, or Cursor.
Deep experience with CoreWeave or comparable GPU cloud platforms, Kubernetes, Docker, NVIDIA Container Toolkit, GPU operators, scheduling, and workload optimization.
Experience supporting NVIDIA Omniverse, Isaac Sim, Isaac Lab, Newton, Cosmos, Replicator, training, inference, and synthetic-data workloads.
Experience with CUDA-aware systems, multi-GPU execution, distributed workloads, job queues, autoscaling, and GPU utilization monitoring.
Experience with telemetry, time-series, sensor, image, video, LiDAR, segmentation, depth, map, and simulation ground-truth data.
Experience with SQL, NoSQL, object storage, time-series databases, Kafka, Spark, Flink, or comparable technologies.
Experience with Terraform, Helm, GitOps, CI/CD, secrets management, security controls, and reproducible environments.
Experience with OpenTelemetry, Prometheus, Grafana, logging, tracing, cost monitoring, and service-level objectives.
Knowledge of statistical validation, uncertainty, tolerance analysis, model correlation, and traceable acceptance criteria.
The candidate must be based in Irving, Texas (Dallas).

Nice To Haves

Seven or more years of software, data, platform, cloud, HPC, or infrastructure engineering experience.
Five or more years building production data pipelines or accelerated-computing platforms.
Experience processing large-scale machine telemetry, sensor, simulation, or Physical AI datasets.
Experience designing secure, reproducible GPU platforms for multiple engineering teams.
Preferred technology stack includes CoreWeave, Kubernetes, NVIDIA GPUs, CUDA, Docker, Terraform, Helm, Omniverse, Isaac Sim, Newton, Replicator, Cosmos, Prometheus, and Grafana.

Responsibilities

Architect machine-data and simulation-data ingestion, processing, curation, storage, and serving pipelines.
Define data models connecting machines, digital twins, environments, scenarios, datasets, models, and results.
Establish verification and validation frameworks, correlation methods, automated regression tests, and quality gates.
Build synthetic-data generation, automatic annotation, dataset lineage, versioning, and reproducibility capabilities.
Architect scalable NVIDIA GPU execution across CoreWeave, on-premises clusters, cloud, and HPC environments.
Operate batch simulation, distributed training, inference, rendering, and synthetic-data workloads using Kubernetes.
Define observability, security, workload scheduling, cost controls, resource quotas, and failure-recovery practices.
Establish infrastructure as code and CI/CD for simulation services, models, datasets, and GPU workloads.
Own a major technical capability across multiple teams and programs, translating ambiguous business and engineering problems into scalable architectures and production software.
Lead architecture reviews, technical investigations, design decisions, and engineering standards.
Balance research and prototyping with secure, maintainable, production-ready delivery.
Mentor senior and developing engineers and support technical hiring and onboarding.
Partner across simulation, robotics, autonomy, controls, AI, data, cloud, product, and customer-facing organizations.
Establish a reference architecture and technical roadmap; deliver reusable platform components and measurable outcomes within the first 12 months.
Improve quality, automation, scalability, and cross-team adoption while reducing duplicated solutions, and develop technical depth through mentoring and standards.