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PADO

GPU & AI Infrastructure - Reserved Deal Desk Broker

  • Confirmed live in the last 24 hours
  • No salary listed
  • Mid level
  • Remote, United States
  • 3+ yrs exp
  • Added today

About this role

GPU & AI Infrastructure – Reserved Deal Desk Broker

Independent Contractor

PADO AI Orchestration, Inc., a new venture born out of LG Electronics and backed by LG NOVA, is revolutionizing energy management for the modern AI data center. We are on a mission to maximize Compute Per Megawatt™ by bridging the gap between infrastructure constraints and the explosive demand for AI-driven compute.

That mission has a commercial front door: PADO’s GPU marketplace. We broker compute between the neocloud and aggregator supply base and the AI-native companies that need capacity now — moving from on-demand and spot into the reserved market, where the real contracts get written. With LG’s backing in BESS storage, high-efficiency chillers, and liquid cooling, we bring credibility to the supply side that a broker alone does not have.

We are seeking a GPU & AI Infrastructure – Reserved Deal Desk Broker to run transactions across both sides of that marketplace. This is not an SDR seat and it is not account management. You will source supply, source demand, match them, price the spread, and carry the deal to signature. Our buyers are the ones the hyperscalers cannot serve — AI-native platforms, defense and robotics programs, biotech, and capital markets firms whose compliance requirements exclude standard hyperscaler options, and whose infrastructure decisions are made at the board level. If you have spent the last two years living in GPU pricing sheets and know who actually has inventory this quarter, this is a desk built for you.

Responsibilities:

  • Supply-Side Sourcing: Build and maintain a live supplier network across neoclouds, aggregators, marketplaces, and colocation-backed operators. Negotiate reserved capacity pricing, prepayment terms, and service levels across H100, H200, B200/B300, GB200/GB300, RTX PRO 6000 Blackwell, and AMD Instinct MI300X/MI355X.
  • Demand-Side Origination: Source and qualify AI-native buyers — inference platforms and foundation model developers, defense technology, robotics and simulation, biotech, and quantitative finance — and translate their workloads into a capacity specification a supplier can actually quote.
  • Transaction Matching: Match workload requirements — interconnect, bare-metal access, region, term length, memory and network profile — against available inventory. Know when a cluster genuinely fits a workload and when it does not.
  • Multi-Stakeholder Deal Navigation: Run procurement cycles that require simultaneous commercial alignment across engineering leaders, CFOs, founders, compliance officers, and board or investment stakeholders. Reserved commitments with significant upfront capital are decided at the board level, not by a single champion.
  • Spread and Margin Ownership: Price to a target spread and own the gross margin on every transaction you originate. You will be measured on realized margin, not on gross contract value.
  • Commercial Structuring: Negotiate prepayment and escrow mechanics, payment timing, SLA pass-through, and non-circumvention terms, including export control and cross-border deployment constraints. Work with PADO’s counsel to keep upstream and downstream agreements mirrored so risk does not sit with PADO.
  • Counterparty Diligence: Assess offtaker creditworthiness, supplier delivery risk, and concentration risk before any capacity is committed. Reserved capacity is a balance-sheet commitment, and the discipline not to commit it prematurely is part of the job.
  • Deal Registration and Pipeline Hygiene: Maintain high standards for Salesforce hygiene and provide accurate forecasting to the executive team. Deal registration governs both PADO’s exposure and your commission crediting.
  • Market Intelligence: Track spot and reserved pricing by chip, region, and term, and feed pricing signal back into PADO’s marketplace and product roadmap.
  • Industry Representation: Serve as a PADO brand ambassador at major trade shows and industry events.

Qualifications:

  • Live Network: 3+ years of full-cycle enterprise sales in GPU or compute brokering, neocloud sales, data center capacity sales, or infrastructure and commodity trading. Existing relationships on the supply side, the demand side, or both are the single most important qualification for this role.
  • Market Fluency: Current on GPU spot and reserved pricing, and fluent in what actually moves it — chip generation, interconnect, bare metal versus virtualized, region and power cost, term length, and prepayment structure.
  • Technical Literacy: Able to qualify a workload without an engineer in the room. You understand ML training environments and foundation model infrastructure, AI infrastructure failure modes, when multi-GPU sharding and NVLink matter and when they do not, and why a fast network without a shared filesystem can still be the wrong cluster.
  • Reserved Compute Planning: Comfortable structuring long-term commitments — one- to five-year terms carrying 25–50% upfront capital — alongside prepayment and escrow, SLA and service-credit regimes, and term-versus-price tradeoffs. You know the difference between GMV and margin, and you work the second one.
  • Regulated and Constrained Environments: Working knowledge of export controls, cross-border AI deployment, and data residency, and the requirements that follow from them — compute segregation for defense programs, auditable GPU cost structures for pre-IPO companies, and hardware-level isolation for proprietary model training in capital markets.
  • Commercial Judgment: You know when not to do a deal. Reserving inventory against unsigned demand is how brokers go out of business, and we expect you to hold that line even when a quarter is thin.
  • Sales Discipline: Rigorous qualification methodology (MEDDIC/MEDDPICC or equivalent) and the ability to build account intelligence from live business signals, infrastructure decision triggers, and buyer-specific friction points.
  • AI-Augmented Professionalism: Beyond understanding AI/ML workloads and what they demand from infrastructure, you proactively use AI and go-to-market tooling — Salesforce, Clay, Apollo.io, Gong, and modern AI assistants — to streamline sourcing, automate counterparty research, and compress your own deal cycle.
  • Startup Resilience: Self-motivated and comfortable in a high-growth environment where you are expected to help build the “playbook,” not just run it.
  • Education: Bachelor’s degree in Business, Engineering, or a related technical field, or equivalent market experience. In this role, the network and the judgment matter more than the credential.

Why Join PADO?

  • The LG Advantage: Join a nimble, venture-backed startup that has the global scale and hardware engineering might of LG Electronics behind it. On the supply side, that name opens doors a standalone broker cannot.
  • A Live Marketplace: PADO’s marketplace is launching with real supplier inventory and a real demand pipeline through the LG ecosystem. You are not building a desk from an empty book.
  • Earning Potential: We believe in rewarding our deal makers. This role features a competitive monthly retainer plus commission, credited on the margin you actually realize and paid as PADO collects. Per-transaction caps apply.
  • Own the Desk: This is the first dedicated deal desk seat at PADO. You will define how the reserved book gets sourced, priced, and papered.
  • Supportive Culture: Collaborate with a team of experts in energy, AI, and data center operations who are passionate about the next industrial paradigm.

Engagement: Independent Contractor (1099)

Location: Remote

Travel: ~25% of time

Compensation: Monthly retainer, plus commission credited on realized gross margin.

Apply: [email protected]

PADO AI Orchestration, Inc. is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Written by PADO. Original job post

Skills mentioned

  • Machine Learning
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