Nvidia Senior Developer Relations Manager Energy - Power Generation Ecosystem 

United States, Texas 

559623798

NVIDIA is searching for an outstanding Developer Relations leader to build a robust partner and developer ecosystem of GPU-accelerated applications across the power generation industry. Our Energy team is responsible for the solution stack and go-to-market strategy that brings accelerated computing and AI to thermal (gas/steam), nuclear (large reactors and SMRs), renewables (solar, wind, hydro), and grid-connected storage. This role sits at the intersection of engineering and business: partnering with leading OEMs and ISVs to co-create digital twins, accelerate physics-based simulation, and deploy AI that improves reliability, safety, and efficiency. Expect to span on-prem, edge, and secure/air-gapped environments while translating real-world plant constraints into platform requirements. If you thrive on turning ground breaking GPUs and SDKs into production-grade outcomes for sophisticated power assets, you’ll feel right at home.

What you’ll be doing:

• Leading technical relationships with CTOs, product managers, and developers across power generation partners including nuclear/SMR, gas/steam, renewables, hydro, and storage—to accelerate adoption of NVIDIA platforms (CUDA, Omniverse, Modulus/Physics-ML, RAPIDS, Triton/TensorRT, NIM, Earth-2, Clara, Jetson/IGX, DGX/GB200, networking, edge/embedded).

• Shaping “blueprint” reference solutions for plant digital twins and balance-of-plant optimization, predictive maintenance and anomaly detection, human-in-the-loop operations, advanced controls, and accelerated design/simulation workflows.

• Driving SDK integrations, joint roadmaps, and evidence-based product feedback loops; influencing NVIDIA platform priorities with partner requirements, important metrics, and performance benchmarks.

• Producing high-quality developer content sample code, reference architectures, and how-to guides—backed by workshops, enablement sessions, and conference talks (e.g., GTC and industry forums).

• Orchestrating multi-party solutions with ISVs, OEMs, SIs, and cloud providers that support engineering design, qualification, licensing, and plant operations.

• Defining and tracking success metrics (design-ins, SDK integrations, POC→production conversion, content adoption, training completions, co-marketing results) and reporting clear progress to partners.

• Partnering with Sales and Business Development to land lighthouse deployments and scale wins across global fleets while ensuring enterprise-grade security, safety, and reliability (model validation, data governance, air-gapped/edge patterns).

What we need to see:

• BS or MS in Computer Science,Electrical/Mechanical/NuclearEngineering, or related field or equivalent experience.

• 8+ years overall in developer-facing or solution roles (DevRel, solutions/field engineering, product/tech marketing, application engineering) delivering AI/ML, simulation/HPC, or industrial software.

• 2+ years leading multi-functional technical initiatives or developer programs (people management or strong matrix leadership).

• Domain familiarity with power-gen operations (plant systems, procedures, safety culture, outage/maintenance cycles) and industrial IoT/OT environments.

• Demonstrated ability to build developer content and communicate effectively with both executives and deep technical audiences.

• Comfort operating in regulated, critical settings (secure/air-gapped, redundant architectures) and collaborating on cybersecurity practices.

• Willingness to travel ~25–40% for partner sites and industry events.

Ways to stand out from the crowd:

• Direct work with Siemens Energy, Hitachi Energy, Westinghouse, GE Hitachi, Rolls-Royce SMR, or SMR start-ups.

• Proven acceleration ofCAE/CFD/FEA/neutronics/thermal-hydraulicsor physics-ML workloads on GPUs, with published benchmarks or case studies.

• Knowledge of nuclear/quality standards and regulatory frameworks (e.g., NQA-1, 10 CFR 50 App. B, IEC/ISO safety) and their impact on software qualification and ML deployment.

• Open-source contributions, reference architectures, or widely adopted tutorials in industrial/energy domains.

• Experience delivering edge AI in harsh/regulated environments (air-gapped, redundant, SIL-rated) and integrating with OT/ICS or exposure to autonomous control/finite-state automation for plant operations.