Senior PM: Hardware Accelerators for OpenStack/OpenShift

Jobtailor; Boston (MA); On-site; USD 150,000 - 230,000; Full time

Job summary

Jobtailor in Boston seeks a senior technical product manager for hardware accelerator virtualization, focusing on Red Hat OpenStack and OpenShift. You will drive end-to-end projects, ensure GPU enablement, and coordinate across engineering, quality, and hardware partners to meet scope, schedule, and budget.

You will translate AI/ML workload requirements into roadmaps, define virtualization architecture requirements for GPU passthrough, and lead kernel-level integration and driver compatibility

Qualifications

• Master’s degree with five years of relevant experience, or bachelor’s with seven years of relevant experience.
• Three years of experience with hardware accelerators, including GPUs and FPGAs, applied to AI/ML workloads
• Three years of experience with AI/ML technologies and frameworks, including classifiers, PyTorch, TensorFlow, and LLMs
• Three years of experience defining virtualization architecture requirements across OpenStack Nova, KVM/libvirt, and Kubernetes for GPU passthrough, partitioning, and performance tuning
• Three years of experience as a technical product manager driving enterprise software solutions, including cloud infrastructure or virtualization platforms
• Two years of experience with Red Hat OpenShift Platform, Red Hat OpenStack Platform, VMware vSphere, or other enterprise infrastructure solutions
• Two years of experience with Agile development processes
• Two years of experience overseeing kernel-level hardware-enablement integration, driver compatibility, and upgrades
• Two years of experience with management frameworks for various accelerator types

Responsibilities

• Manage end-to-end execution of hardware accelerator virtualization projects on Red Hat OpenStack and OpenShift
• Ensure GPU enablement, resource scheduling, and delivery of Compute/Cyborg components stay within scope, schedule, and budget
• Confer with engineering, quality, and hardware partner teams to resolve technical blockers in driver alignment, secure boot compliance, and upgrade compatibility
• Monitor milestones for AI/ML workload optimization, including GPU passthrough, partitioning, and performance tuning
• Assess customer and market requirements for GPUs and FPGAs in AI/ML and HPC workloads and translate them into roadmap priorities
• Develop and update OpenShift hardware-enablement project plans, objectives, specifications, schedules, and staffing needs
• Prepare status reports on hardware trends and virtualization architecture requirements to inform platform strategy
• Establish communication plans across RHEL, OpenShift, OpenStack, and external hardware vendors
• Submit production-grade accelerator features after validating scalability and security
• Direct engineering and quality personnel validating scalable, secure accelerator features
• Perform risk assessments and develop mitigation strategies for integration, lifecycle management, upgrades, and secure boot compliance
• Develop a WBS for Red Hat OpenStack Platform Compute and Cyborg component delivery
• Negotiate with GPU vendors and FPGA suppliers for early accelerator access and joint roadmap commitments

Skills

Hardware accelerator mgmt

GPU/FPGA integration

Kubernetes

AI/ML workloads

Virtualization architecture

Agile development

Technical product management

Cloud infrastructure

Driver compatibility

Risk assessment

Education

Master’s degree in Mechanical Engineering/Tech Management/Business Admin

Bachelor’s degree in related fields

Tools

Red Hat OpenShift

Red Hat OpenStack

VMware vSphere

KVM/libvirt

PyTorch

TensorFlow

Cyborg/Compute components

Secure Boot Compliance

Job description

Jobtailor in Boston seeks a senior technical product manager for hardware accelerator virtualization, focusing on Red Hat OpenStack and OpenShift. You will drive end-to-end projects, ensure GPU enablement, and coordinate across engineering, quality, and hardware partners to meet scope, schedule, and budget.

You will translate AI/ML workload requirements into roadmaps, define virtualization architecture requirements for GPU passthrough, and lead kernel-level integration and driver compatibility