Ai Root Jobs (NOW HIRING)

THE ROLE: 

We are seeking a Technical Program Manager to lead execution of AI cluster engineering programs with deep focus on GPU platforms, rack-level solutions, and AI Cluster validation. This role is responsible for driving end-to-end delivery from GPU + server integration through rack bring-up, scale testing, failure analysis, and system debug closure, ensuring platform readiness for hyperscale and enterprise AI deployments. 

This role operates at the intersection of hardware, firmware, networking, and scale-test execution, and requires strong technical depth combined with disciplined program execution. 

THE PERSON:

You are a hands-on TPM who thrives in complex, fast-moving ecosystems, and can connect deep technical details to crisp program plans, executive reporting, and customer outcomes. You are comfortable driving execution in bring-up and EVT/DVT/PVT working closely with engineers to root-cause issues, unblock debug, and make data-driven tradeoffs to keep programs moving. You bring urgency, ownership, and clarity to ambiguous problem spaces and can communicate effectively from lab floor to executive review. 

KEY RESPONSIBILITIES:

Program Leadership & Execution 

• Define, plan, and drive program plans for AI infrastructure systems validation and readiness, including server integration, rack bring-up, and cluster-scale deployment readiness.  

• Create and maintain core PM artifacts: schedules, dependency maps, resource forecasts, risk/issue logs, and program dashboards/status reports.  

• Identify and drive mitigation plans for issues/risks, including cross-team escalations and corrective actions across multiple engineering areas.  

• Drive regular execution reviews with engineering teams and provide concise, data-driven updates to senior leadership. 

GPU & Platform Execution 

• Own program execution for GPU-based AI platforms, spanning system bring-up, qualification, scale readiness, and deployment validation across server, rack, and cluster levels. 

• Drive alignment across GPU, CPU, firmware, BIOS/BMC, and system teams to ensure readiness for scale testing and customer workloads. 

• Track platform issues, and debug dependencies; ensure risks are clearly documented, owned, and mitigated. 

AI Rack / Cluster Validation 

• Own program planning and execution for multi-node and multi-rack scale testing, including test strategy, scheduling, coverage tracking, and readiness gates. 

• Lead end-to-end delivery of rack-level AI solutions, including compute trays, switch trays, cabling, power, cooling, and management infrastructure. 

• Ensure rack bring-up plans are executable, resourced, and gated with clear entry/exit criteria across EVT, DVT, and scale phases. 

• Drive coordination across lab operations, infrastructure, and engineering teams to unblock rack access, power, networking, and test readiness. 

• Partner with scale, performance, and automation teams to ensure workloads, stress tests, and regressions plans are ready before hardware arrives. 

Debug, Failure Analysis & Risk Management 

• Act as the execution lead for platform debug, coordinating across engineering teams to ensure fast triage, root-cause analysis, and resolution of system-level issues. 

• Track high-impact failures (GPU, HSIO, FW, rack, network) through debug forums ensuring clear ownership and closure plans. 

• Balance debug depth vs. program timelines, escalating tradeoffs when needed and ensuring leadership has a clear view of risk and impact. 

System Rack Debug & Incident Management 

• Lead end-to-end system rack and cluster-level debug activities during validation, deployment, and fleet operations, driving rapid fault isolation and root-cause analysis across hardware, firmware, networking, and software domains. 

• Own incident management for critical deployment and validation issues, coordinating cross-functional war rooms and executive communications to ensure timely resolution. 

• Define key operational metrics such as Mean Time to Detect (MTTD), Mean Time to Mitigate (MTTM), Mean Time to Resolution (MTTR), incident recurrence rates, and fleet health indicators. 

• Drive post-incident reviews (PIRs), root-cause analysis (RCA), corrective actions, and long-term preventive measures to reduce Sev1/Sev2 incident recurrence and improve fleet reliability and deployment readiness. 

• Champion Agentic AI and AIOps solutions for automated incident triage, log analysis, root-cause identification, and operational workflow automation at scale 

Agentic AI Transformation 

• Identify opportunities to leverage Agentic AI capabilities across validation and debug workflows. 

• Drive development and rollout of AI agents for:  

• • Automated triage 
  •  Log analysis 
  •  Root-cause identification
  • Knowledge retrieval
  • Test orchestration
  • Incident management 

• Partner with data engineering teams to integrate AI-powered operational tools. 

• Establish success metrics and measurable business impact for agent-based automation initiatives. 

• Champion AI-first operational workflows across engineering organizations. 

REQUIRED QUALIFICATIONS:

• Experience leading complex hardware or AI infrastructure programs with ownership across bring-up, validation, and deployment phases. 

• Strong technical understanding of GPU-based AI systems, rack architectures, and datacenter infrastructure. 

• Proven ability to manage ambiguity, drive debug execution, and lead cross-functional teams without direct authority. 

• Strong written and verbal communication skills, including executive-level status reporting. 

• Proficiency with program management and execution tools (Jira, Confluence, dashboards, Excel/PowerPoint). 

PREFERRED QUALIFICATIONS:

• Hands-on experience with GPU cluster scale testing, system stress, or performance validation. 

• Familiarity with rack-level bring-up, power/cooling constraints, networking, and failure modes at scale. 

• Experience working through hardware/firmware debug cycles in pre-production or customer-facing environments. 

• Experience managing fleet-scale validation and deployment of AI, cloud, HPC, or hyperscale infrastructure. 

• Strong understanding of infrastructure observability, telemetry pipelines, log analytics, and incident management frameworks. 

ACADEMIC CREDENTIALS:

• Bachelor’s or master’s degree systems, EE, CS, or related engineering discipline. 

• PMP, Scrum Master, or equivalent program management training. 

LOCATION: Austin, TX