Technical PM - BioNeMo Inference Platform

NVIDIA Corporation; Santa Clara, Northern (CA, KY); Hybrid; USD 148,000 - 224,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation in Santa Clara, CA seeks a technical Product Manager to lead BioNeMo Inference, defining how developers and enterprises deploy, operate, and scale AI inference workloads on NVIDIA NIM.

You’ll collaborate with engineering, research, cloud, and platform teams to deliver production-ready inference experiences, focusing on batching, throughput, latency, and multi-node scaling.

Qualifications

• 5+ years of industry experience.
• 3+ years in software engineering or a deeply technical role.
• 2+ years in product management.
• Master’s degree or equivalent experience.

Responsibilities

• Define product vision, strategy, and roadmap for BioNeMo inference products.
• Collaborate with engineering, research, solution architects, cloud and product teams.
• Define requirements for inference optimization: batching, latency, throughput, and scaling.
• Partner with platform teams for cloud and Kubernetes deployments.
• Develop APIs, SDKs, containers, Helm charts, and reference architectures.
• Engage with early customers to gather feedback and priority requirements.
• Establish metrics for adoption, usability, performance and reliability.
• Drive cross-functional launches with marketing, sales, and architects.
• Define the boundary between open-source models, NVIDIA NIMs, and enterprise deployment.

Skills

Technical PM

AI/ML inference

Developer experience

Cloud & deployment

Leadership

Education

Master's degree in related field

Tools

Docker

Kubernetes

Helm

CI/CD

Public cloud

Job description

NVIDIA Corporation in Santa Clara, CA seeks a technical Product Manager to lead BioNeMo Inference, defining how developers and enterprises deploy, operate, and scale AI inference workloads on NVIDIA NIM.

You’ll collaborate with engineering, research, cloud, and platform teams to deliver production-ready inference experiences, focusing on batching, throughput, latency, and multi-node scaling.