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Mistral is building full-stack AI solutions spanning frontier models, developer tools, applications, and compute infrastructure. The company partners with enterprises across finance, manufacturing, defense, healthcare, and the public sector to co-create customized AI systems.
This Revenue Operations Lead role sits at the intersection of Mistral's Inference business operations and commercial strategy. Unlike typical software businesses, Mistral's inference revenue is constrained by GPU fleet capacity and driven by the blended cost of GPUs serving each workload—both of which fluctuate weekly. You will own the commercial data, models, processes, and operating cadence that answer three core questions with real numbers:
1. What do we have to sell?
2. What should we sell it for?
3. What do we make when we do?
You will partner closely with the Head of Digital Native Sales and serve as the commercial operating lead across Sales, Data Science, Inference Infrastructure, Finance, and Product. This is a build role; much of the commercial infrastructure does not yet exist.
**Key Responsibilities:**
**Capacity and Demand:** Build and own an end-to-end inference demand forecast combining pipeline, contracted commitments, and consumption trends. Combine capacity signals with pipeline demand to surface shortages, unused capacity, and supply-demand mismatches. Translate customer requirements into structured inputs for Data Science and Infrastructure teams to assess workload impact and feasibility; help prioritize opportunities by revenue, margin, fit, timing, and strategic value.
**Pricing and Margin:** Publish regular updates to GTM and Finance on available supply and recommended pricing, 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. Build and own margin sensitivity models showing how workload, utilization, contract structure, and discounting affect deal and portfolio economics.
**Data Infrastructure and GTM Telemetry:** Build commercial telemetry for Inference covering usage, consumption patterns, expansion, churn signals, and revenue leading indicators. Connect capacity and usage data with pipeline, contracts, pricing, and revenue; build self-serve reporting for GTM, Finance, and leadership. Partner with Finance on long-range planning, connecting demand forecasts to capacity and capex planning.
**Core Revenue Operations:** 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, translating them into pricing and positioning input.
**Cross-Functional Leadership:** Drive alignment across Sales, Data Science, Infrastructure, Finance, and Product on demand, capacity, pricing, and economics. Ensure customer demand reaches technical teams early to inform capacity decisions; build structured feedback loops from requests, adoption patterns, blocked deals, and losses into Product and Engineering.
**Requirements:**
- Experience in revenue operations, business operations, strategic finance, or investment banking in a business with real, variable cost of goods (cloud infrastructure, compute, telco, logistics, or similar). Pure SaaS RevOps alone is not sufficient.
- Strong SQL and ability to build your own data models from 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.
- Technical fluency sufficient to hold substantive conversations with infrastructure engineers about GPUs, utilization, serving efficiency, and capacity—or clear evidence of ability to acquire this knowledge quickly.
- Comfort operating with incomplete data, with bias toward shipping a rough answer this week over a perfect one next quarter.
- Strong cross-functional leadership and ability to influence technical and commercial teams without direct authority.
**Ideal Additional Skills:**
- Familiarity with the inference market: provisioned throughput, per-token pricing, batch vs. real-time serving, and major provider pricing models.
- Experience with consumption- or usage-based revenue models.
- Salesforce, BigQuery, and BI tooling (Metabase).
- Prior experience sitting between technical and commercial teams, trusted by both sides.
