Solutions Architect, Inference Deployments

Responsibilities

• Lead architecture discussions with customers and co-design scalable inference systems with product and engineering teams. Define technical direction, integration milestones, and deployment plans across edge and cloud environments.
• Deploy, test, and profile speech and action models for robotics and automation on edge devices. Optimize latency, throughput, and memory use within device constraints.
• Lead optimization of synthetic data workflows spanning generation, augmentation, filtering, and data movement. Improve throughput and cost per usable output while preserving data quality.
• Guide production inference deployments, including configuration, monitoring, capacity planning, performance testing, release validation, and rollback.
• Resolve complex issues across models, runtimes, and infrastructure.
• Evaluate model optimization methods, including quantization, kernel selection, and batching. Validate application quality, explain performance tradeoffs, and use findings to inform product and engineering priorities.
• Drive customer and field enablement through deployment recipes, demos, onboarding materials, and operating guides that help teams adopt and scale NVIDIA technologies.
• What we need to see
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
• 5+ years of professional experience in software or hardware engineering, developer relations, technical partnerships, solutions architecture, or product management, including 3+ years of direct, hands-on experience in AI inference or ML systems.

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