Job description

NVIDIA is advancing the frontier of AI for biology with BioNeMo, bringing accelerated computing and generative AI to biomolecular research and drug discovery. We are seeking a technical Product Manager to lead BioNeMo Inference. You will define how developers, researchers, and enterprise platform teams deploy, operate, and scale biomolecular AI inference workloads. This role sits at the intersection of AI infrastructure, developer experience, and product execution: translating the needs of model developers and end users into simple, reliable inference products built on NVIDIA NIM and accelerated computing. A biology or healthcare background is not required. We are looking for a strong technical PM who understands the fundamentals of AI inference serving and model deployment and is eager to apply them to a new and high-impact domain. What you’ll be doing: • Define product vision, strategy, and roadmap for BioNeMo inference products, including NIM-based deployment, performance optimization, scalability, and developer onboarding. • Work closely with engineering, research, solution architects, cloud, and product teams to translate model capabilities into production-ready inference experiences. • Define requirements for inference optimization: batching, throughput, latency, GPU utilization, multi-GPU and multi-node scaling, caching, scheduling, observability, and reliability. • Partner with platform teams to ensure BioNeMo inference products deploy cleanly across cloud and enterprise environments, including Kubernetes-based environments. • Develop the developer experience across APIs, SDKs, containers, Helm charts, reference architectures, documentation, and examples. • Engage directly with early customers and partners to understand workflows, validate product direction, and turn feedback into prioritized requirements. • Establish product metrics for adoption, usability, performance, and operational quality; use data and customer insight to improve the product. • Drive cross-functional product launches with marketing, developer relations, sales, and solution architects. • Help shape the product boundary between open-source biomolecular models, NVIDIA NIMs, and enterprise-ready deployment and support! What we need to see: • 5+ years of industry experience, ideally including 3+ years in software engineering or a deeply technical role and 2+ years in product management. • Master’s degree (or equivalent experience) in Electrical, Mechanical, Materials, or other related fields. • Demonstrated experience defining and shipping technical products, platforms, APIs, SDKs, developer tools, or cloud services. • Strong understanding of AI/ML inference systems and deployment tradeoffs, including model serving, GPU infrastructure, batching, throughput, latency, scaling, and cost-performance optimization. • Familiarity with containers and cloud-native infrastructure, including Docker, Kubernetes, Helm, CI/CD, and public-cloud deployment concepts. • Ability to work fluently with engineers on system architecture, performance bottlenecks, operational requirements, and roadmap tradeoffs. • Strong customer discovery, product requirement definition, prioritization, and roadmap-management skills. • Excellent written and verbal communication skills for both technical and non-technical audiences. • A collaborative, self-starting approach and the ability to influence across highly matrixed teams. Ways to stand out from the crowd: • Experience launching and scaling AI inference, model-serving, MLOps, or GPU-accelerated infrastructure products. • Hands-on expertise with modern AI inference and orchestration platforms such as NVIDIA NIM, Triton, TensorRT-LLM, vLLM, Ray, KServe, or Kubeflow. • Proven success optimizing production inference workloads, including performance, scalability, observability, and multi-tenant serving. • Experience building developer platforms or open-source products with strong ecosystem adoption and integrations. • Ability to partner with research teams to productize frontier AI models, including familiarity with biomolecular and scientific foundation models. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 224,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 8, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

We are looking for a strong technical PM who understands the fundamentals of AI inference serving and model deployment and is eager to apply them to a new and high-impact domain.
Define product vision, strategy, and roadmap for BioNeMo inference products, including NIM-based deployment, performance optimization, scalability, and developer onboarding.
Work closely with engineering, research, solution architects, cloud, and product teams to translate model capabilities into production-ready inference experiences.
Define requirements for inference optimization: batching, throughput, latency, GPU utilization, multi-GPU and multi-node scaling, caching, scheduling, observability, and reliability.
Partner with platform teams to ensure BioNeMo inference products deploy cleanly across cloud and enterprise environments, including Kubernetes-based environments.
Develop the developer experience across APIs, SDKs, containers, Helm charts, reference architectures, documentation, and examples.
Engage directly with early customers and partners to understand workflows, validate product direction, and turn feedback into prioritized requirements.
Establish product metrics for adoption, usability, performance, and operational quality; use data and customer insight to improve the product.
Drive cross-functional product launches with marketing, developer relations, sales, and solution architects.
Help shape the product boundary between open-source biomolecular models, NVIDIA NIMs, and enterprise-ready deployment and support!
5+ years of industry experience, ideally including 3+ years in software engineering or a deeply technical role and 2+ years in product management.
Master’s degree (or equivalent experience) in Electrical, Mechanical, Materials, or other related fields.
Demonstrated experience defining and shipping technical products, platforms, APIs, SDKs, developer tools, or cloud services.
Strong understanding of AI/ML inference systems and deployment tradeoffs, including model serving, GPU infrastructure, batching, throughput, latency, scaling, and cost-performance optimization.
Familiarity with containers and cloud-native infrastructure, including Docker, Kubernetes, Helm, CI/CD, and public-cloud deployment concepts.
Ability to work fluently with engineers on system architecture, performance bottlenecks, operational requirements, and roadmap tradeoffs.
Strong customer discovery, product requirement definition, prioritization, and roadmap-management skills.
Excellent written and verbal communication skills for both technical and non-technical audiences.
A collaborative, self-starting approach and the ability to influence across highly matrixed teams.
Experience launching and scaling AI inference, model-serving, MLOps, or GPU-accelerated infrastructure products.
Hands-on expertise with modern AI inference and orchestration platforms such as NVIDIA NIM, Triton, TensorRT-LLM, vLLM, Ray, KServe, or Kubeflow.
Proven success optimizing production inference workloads, including performance, scalability, observability, and multi-tenant serving.
Experience building developer platforms or open-source products with strong ecosystem adoption and integrations.
Ability to partner with research teams to productize frontier AI models, including familiarity with biomolecular and scientific foundation models.

Nice-to-haves

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Keywords to mirror

aikubernetesmldockerci/cdllm

Platform: eightfold
Effort: medium

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