Technical Product Manager – Soperator

About This Opportunity

We are representing a confidential AI infrastructure organization in their search for a Technical Product Manager to lead product direction for a Slurm-on-Kubernetes control plane used to orchestrate GPU clusters at scale. This is a remote position working with distributed teams across multiple time zones.

Our client operates at the intersection of cloud infrastructure and AI workload orchestration, enabling ML engineers and research teams to run distributed training and inference across thousands of GPUs. You'll own the product roadmap for a critical open-source platform that combines performance, reliability, and developer experience.

Responsibilities

• Own the complete user journey across GPU cluster orchestration: Slurm workflows, observability dashboards, alerting and notifications, node lifecycle management, and training/inference capacity planning
• Define end-to-end product direction from problem discovery through solution design, delivery, and adoption measurement
• Conduct deep customer discovery through interviews, usage analytics, and workload analysis to identify high-impact product opportunities
• Drive cross-functional execution across platform teams including compute, networking, storage, observability, and IAM
• Translate emerging ML infrastructure patterns into practical product capabilities for production GPU environments
• Define success metrics, use data to prioritize roadmap decisions, and ensure measurable customer and business outcomes
• Lead open-source strategy and execution: shape public roadmap themes, prioritize community-facing capabilities, and drive adoption

What We're Looking For

Required qualifications:

• 3–5+ years in Product Management, ML infrastructure/MLOps, distributed systems, or cloud platform engineering
• Strong technical depth in distributed systems, cloud infrastructure, or ML platforms
• Hands-on familiarity with large-scale ML training and orchestration tools such as Slurm, Kubernetes, or Ray
• Proven track record shipping technically complex products involving multiple engineering teams
• Excellent communication and stakeholder management skills across engineering, research, and customer-facing teams
• Experience with product analytics, data-informed prioritization, and experimentation frameworks
• High ownership mentality, rapid learning velocity, and comfort operating in fast-moving AI infrastructure environments

Bonus qualifications:

• Experience with GPU platforms and HPC primitives: InfiniBand/RDMA, topology-aware scheduling, high-throughput storage systems
• Practical understanding of modern ML training stacks: PyTorch, DeepSpeed, FSDP/ZeRO, NCCL
• Familiarity with efficiency and reliability metrics: Goodput, MFU, failure modes, preemption handling, health checks
• Exposure to large-scale LLM training or inference systems
• Background in observability, performance tuning, or SRE/reliability engineering
• Customer-facing technical experience in solution architecture, technical support, or advisory roles

How We Work With You

When you apply through Get A Job.ai, our talent team will review your profile and conduct an initial screening conversation. If there's a strong match, we'll submit your candidacy to our client for consideration and guide you through their interview process. Please do not contact the client directly—all communication should flow through our recruiting team to ensure the best experience for everyone involved.

Pay

Compensation details will be discussed during the screening process based on experience and location.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work to ensure fair and equitable treatment throughout our recruitment process.