Principal Software Engineer - Simulation Data, Validation & Infrastructure

Caterpillar•Irving, TX

About The Position

At Caterpillar, technology is used to solve customers’ toughest challenges. Construction autonomy and Physical AI depend on trusted digital twins, high-fidelity simulation, scalable GPU computing, intelligent automation, and rigorous validation. This hands-on technical leadership role is responsible for architecture, critical implementation, engineering standards, cross-team alignment, and mentoring. The role leads the data, validation, and accelerated-computing foundation needed to operate simulation at enterprise scale. It connects evidence from physical machines with virtual-machine behavior through reproducible pipelines and trusted infrastructure. The position is based in Irving, Texas (Dallas); domestic relocation assistance and visa sponsorship are available. The Principal Engineer owns a major technical capability across teams and programs, translating ambiguous business and engineering problems into scalable architectures and production software. The engineer leads architecture reviews and technical investigations, balances prototyping with secure and maintainable delivery, mentors engineers, and partners with teams across simulation, robotics, autonomy, controls, AI, data, cloud, product, and customer-facing organizations.

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.

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.
Experience with the preferred technology stack: 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 that connect 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 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 clear reference architecture and technical roadmap for the capability area.
Deliver reusable platform components and measurable engineering outcomes within the first 12 months.
Improve quality, automation, scalability, and cross-team adoption while reducing duplicated solutions.
Develop technical depth across the broader engineering organization through mentoring and standards.