TL;DR

Technical Program Manager (AI): Drive programs managing cluster orchestration and model benchmarking for AI workloads on GPU-accelerated infrastructure with an accent on distributed systems, scheduling, and performance evaluation. Focus on designing and scaling orchestration systems, benchmarking frameworks, and cross-team collaboration to optimize AI workload efficiency and reliability.

Location: Hybrid in Livingston, NJ; New York, NY; Sunnyvale, CA; San Francisco, CA; Bellevue, WA, with remote considered for candidates >30 miles from office

Salary: $161,000–$237,000

Company

%hirify_global% is a publicly traded AI cloud infrastructure company providing GPU-accelerated platforms for AI labs, startups, and enterprises.

What you will do

Lead end-to-end program management for cluster orchestration initiatives including Kubernetes and Slurm-on-Kubernetes.
Drive cross-functional efforts to deliver orchestration capabilities for distributed AI training and inference workloads.
Define roadmaps for cluster utilization, scheduling efficiency, and workload resilience with engineering and product teams.
Own execution of model benchmarking programs measuring performance, throughput, latency, and cost across hardware and environments.
Manage cross-team dependencies, performance testing, and release management for orchestration systems.
Build dashboards and feedback loops to improve scheduling efficiency and GPU utilization.

Requirements

Must be a U.S. person or eligible for export-controlled information access per U.S. Government regulations.
Bachelor’s degree or equivalent experience with 8+ years in technical program management in distributed systems, cloud infrastructure, or AI/ML platforms.
Proven experience with large-scale orchestration or scheduling systems like Kubernetes, Slurm, or similar.
Strong technical fluency in distributed computing, job scheduling, Kubernetes orchestration, and benchmarking methodologies.
Exceptional communication and collaboration skills to align multiple teams on complex initiatives.

Nice to have

Experience with model benchmarking frameworks and profiling tools (e.g., MLPerf, vLLM benchmarks).
Understanding of GPU types, model parallelism, and distributed training/inference performance trade-offs.
Experience designing benchmarking data pipelines and visualization tools.
Background in building operational maturity and visibility in multi-team technical organizations.

Culture & Benefits

Medical, dental, and vision insurance fully paid by employer.
Company-paid life insurance and disability coverage.
Flexible spending and health savings accounts.
Tuition reimbursement and employee stock purchase program participation.
Paid parental leave and family-forming support.
401(k) with employer match and flexible PTO.
Casual work environment with catered lunches and quarterly team gatherings.

Hiring process

Onboarding at one of the company hubs within the first month.
Interviews and evaluations focused on technical expertise and collaboration skills.
Consideration of export control compliance requirements during hiring.