PM, Compute Platform

PADO AI

Orchestration, Inc., a groundbreaking venture backed by LG NOVA, is revolutionizing energy management for the modern data center. Pado is led by a team of serial tech entrepreneurs, with over 60+ years of experience in the energy and AI space. We leverage proprietary AI and ML to empower data centers to optimize power consumption, enhance resilience, and significantly improve operational efficiency. As the demand for AI-driven compute power escalates, data centers face unprecedented energy challenges. PADO offers the intelligent orchestration needed to navigate this complex landscape.

PADO is backed by LG NOVA, the Silicon Valley-based North American innovation division of LG Electronics.

PADO is searching for a strategic and technical product manager to lead our compute platform. As PM you will own the entire product lifecycle-from identifying market opportunities and defining vision to launching and iterating on features. We are looking for a "builder" who thrives in fast-paced, ambiguous environments, blending commercial acumen with a deep understanding of how software and hardware systems interact.

Responsibilities

• Platform Strategy: Define and execute a long-term compute platform roadmap that has buy-in from the executive team and the backing of the engineering team. Design with flexibility to drive value for multiple segments of data centers and AI infra companies.
• Drive Compute Optimization: Define capabilities that raise GPU utilization and goodput: smarter queueing and preemption, bin packing, fractional and multi-tenant GPU sharing, and power-aware placement that fits more work inside the same MW.
• Turn Emerging Tech into an Adopted Product: Take new research and capabilities, such as agent-driven optimization and workload placement, and turn them into products customers use, through partnerships, integrations, or open source.
• Technical Sales & ROI: Partner with sales to lead discovery sessions and build credible ROI/TCO arguments tied to GPU utilization, compute throughput, and OpEx savings.
• Delivering & Measuring Outcomes: Manage and measure deployments across on-prem and cloud environments, interfacing with DCIM, BMS, and workload scheduling layers.
• Be the Glue: Partner closely with engineering, UX/UI, marketing, sales, and data science teams to ensure features are logical and intuitive. Participate in scrums, sprint planning, and be ready to remove roadblocks across the spectrum to deliver products as fast as possible.
• Market & Customer Research: Lead voice-of-the-customer (VOC) efforts and competitive analysis to identify gaps and new investment opportunities. Be the customer representative and advocate internally to ensure the full team understands client needs and delighters.
• Execution & Delivery: Ensure that products and features can be delivered with high quality and speed. Be the face of PADO to clients, deliver products to clients, and act as a forward-deployed PM/engineer as necessary.
• Data-Driven Decision Making: Track success metrics (KPIs) such as adoption, revenue, and task success to inform future product iterations.

Minimum Qualifications

• Professional Experience: 4-10+ years of experience in product management, particularly with complex technical data products.
• Data Center & AI Compute Domain Expertise: Strong understanding of data center infrastructure (servers, UPS, cooling), power systems (three-phase power, redundancy), or AI/ML frameworks. Solid understanding of HPC and AI cluster orchestration concepts, including cluster management, scheduling, and monitoring.
• Experience with Workload Schedulers: Slurm, Kubernetes scheduling (Kueue, Volcano, or custom schedulers), or similar. You understand queueing, priorities, preemption, gang scheduling, and fair share.
• 0-1 Product Experience: A record of turning ambiguous research or emerging technical capabilities into adopted products through a partnership motion, integrations, or open source.
• AI Forward: Technical fluency in agent architectures, observability, evaluation design, and automated experimentation techniques.
• Analytical Skills: Ability to use data and experimentation (such as A/B testing) to drive product adoption and business decisions.
• Communication Skills: Exceptional written and verbal skills, with the ability to articulate technical concepts to both engineers and C-suite stakeholders.

Nice to Have

• Experience running or building for large GPU clusters (hundreds to thousands of GPUs) for training or inference.
• Familiarity with NVIDIA's cluster stack (NCCL, DCGM, Base Command, Run:ai, DSX) or AMD equivalents (ROCm, RCCL).
• Experience with GPU partitioning and sharing (MIG, time slicing, MPS) or inference serving frameworks (vLLM, Triton, Dynamo).
• Background in power-aware scheduling, workload power profiling, or data center power and cooling constraints.
• Experience with cluster telemetry and observability (Prometheus, DCGM exporter, time-series data).
• Exposure to neocloud or GPU-as-a-service business models, including reserved and on-demand capacity.
• An engineering or computer science degree.