Inference Lead, Mistral AI

• Experience in revenue operations, business operations, strategic finance, or investment banking in a business where cost of goods is real and variable — cloud infrastructure, compute, telco, logistics, or similar. Pure SaaS RevOps is not a fit on its own
• Strong SQL and comfort building your own data models: raw tables to dashboard, without waiting on a data team
• Demonstrated ability to build unit economics and margin models that other functions trust and use
• Enough technical fluency to hold a real conversation with infrastructure engineers about GPUs, utilization, serving efficiency, and capacity — or clear evidence you can get there fast
• Comfort operating with incomplete data, with a bias toward shipping a rough answer this week over a perfect one next quarter
• Strong cross-functional leadership and the ability to influence technical and commercial teams without direct authority

Desirable

• Our Inference business works differently from a typical software business. What we can sell is capped by GPU fleet capacity, and what we earn depends on the blended cost of the GPUs serving each workload. Both can change week to week
• This role sits between the people who run the fleet and the people who sell it. You will be the person who can answer, at any point in the quarter, three questions with real numbers behind them:
• What do we have to sell?
• What should we sell it for?
• What do we make when we do?
• You will own the commercial data, models, processes, and operating cadence behind these decisions, partnering closely with the Head of Digital Native Sales and serving as the commercial operating lead across Sales, Data Science, Inference Infrastructure, Finance, and Product
• This is a build role. Little of the commercial infrastructure you need exists today
• Build and own an end-to-end inference demand forecast combining pipeline, contracted commitments, and consumption trends into a forward-looking view of expected demand
• Combine capacity signals with pipeline demand to show what is available to sell and surface shortages, unused capacity, and supply-demand mismatches
• Translate customer requirements into structured inputs that Data Science and Inference Infrastructure can use to assess workload impact and feasibility, and help prioritize opportunities by revenue, margin, fit, timing, and strategic value
• Publish regular updates to GTM and Finance on available supply and recommended price, informed by market rates for provisioned throughput and per-token pricing
• Partner with Finance and Pricing to build rate cards, target prices, price floors, and discount guardrails GTM can act on
• Build and own margin sensitivity models showing how workload, utilization, contract structure, and discounting affect deal and portfolio economics
• Build the commercial telemetry for Inference: usage, consumption patterns, expansion, churn signals, and other leading indicators of revenue
• Connect capacity and usage data with pipeline, contracts, pricing, and revenue, and build self-serve reporting that helps GTM, Finance, and leadership make capacity, pricing, and prioritization decisions
• Partner with Finance on long-range planning for Inference, connecting demand forecasts to capacity and capex planning
• Own territory design, ICP definition, data-driven targeting, pipeline management, forecasting, and compensation plan design for the Inference and Digital Native segment
• Track competitive dynamics across inference providers and adjacent players, turning them into pricing and positioning input
• Drive alignment across Sales, Data Science, Inference Infrastructure, Finance, and Product on demand, capacity, pricing, and economics
• Ensure customer demand reaches technical teams early enough to inform capacity decisions, and build a structured feedback loop from requests, adoption patterns, blocked deals, and losses into Product and Engineering