Principal Data Engineer Jobs

About The Position

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.

Benefits

• Medical, dental, and vision benefits
• Paid time off plan, including vacation, holidays, and volunteer time
• 401(k) savings plans
• Health Savings Account (HSA)
• Flexible Spending Accounts (FSAs)
• Health Lifestyle Programs
• Employee Assistance Program
• Voluntary Benefits and Employee Discounts
• Career Development
• Incentive bonus
• Disability benefits
• Life Insurance
• Parental leave
• Adoption benefits
• Tuition Reimbursement
• Domestic relocation assistance