Nscale

Develops and operates AI data centers

GTM Engineer

Sales OperationsConfirmed live in the last 24 hours

Full-timeIn-person

About the Job

Requirements

4–8 years of experience in revenue operations, sales operations, or go-to-market engineering at a business-to-business technology company; early-stage or high-growth experience is ideal.
Hands-on experience implementing and administering a customer relationship management system such as Salesforce or HubSpot, including data models, pipeline design, routing, hygiene, and reporting.
Ability to work with data, application programming interfaces, integrations, enrichment sources, automation tools, and lightweight scripting such as SQL plus Python or similar.
Ability to identify and build high-leverage systems from ambiguous problems without waiting for detailed requirements.
Working understanding of early-stage sales operations, including ideal customer profile definition, prospect research, pipeline management, and handoffs.
Ability to work with sellers, commercial leadership, product, engineering, and finance, translating commercial needs into clear system and data requirements.

Responsibilities

Implement and structure the customer relationship management system from the ground up, including account and opportunity data models, pipeline stages, required fields, routing, process hygiene, and reporting.
Architect and build the go-to-market technology stack and integrations and automations to eliminate manual work.
Own field governance and data integrity with Revenue Enablement, enforcing standards in the system.
Create the go-to-market data layer for pipeline health, deal velocity, forecast accuracy, average selling price by stock-keeping unit, utilization, and capacity sold forward; define, instrument, and validate the data.
Build reporting for the weekly pipeline review, capacity allocation forum, and WPR.
Build executive dashboards for funnel performance, pipeline coverage, and launch adoption.
Build agentic and automated workflows for research, enrichment, account intelligence, signal capture, and routing while preserving human judgment on strategic accounts.
Build internal tools, dashboards, and agents, including initial versions of a competitive intelligence hub and representative-facing agents.
Build automation for deal capture and commercial document assembly as deal desk standardizes its structures.
Mine Nscale ecosystem, open-source community, and platform usage telemetry data to identify prospects, warm paths, and expansion signals.
Turn usage and adoption signals into sales triggers, including identifying scaling customers, customers nearing capacity, and customers whose activity has declined.
Design selling workflows with sellers and build the onboarding path for future sellers with Revenue Enablement.

Desired Qualifications

Experience building go-to-market infrastructure from zero to one before the company had a mature customer relationship management system, documented ideal customer profile, or standardized sales process.
Experience with artificial-intelligence-native go-to-market workflows, including research automation, enrichment, account intelligence, workflow orchestration, or agent building.
Experience at an infrastructure, developer-tools, open-source, or consumption-priced company; familiarity with graphics processing units, Ray, or Kubernetes is a plus.
Experience mining community or product-usage data as commercial intelligence.
Exposure to quota reporting, attainment tracking, or sales compensation operations.

About the role: Nscale's GTM organization is being built right now — and the revenue infrastructure underneath it does not exist yet. The commercial source of truth, the account intelligence, the data layer behind pipeline and forecasting, and every automation on top will be designed, built and shipped by this role, from a blank page. This is a hands-on builder role that combines revenue operations judgment with GTM engineering: you decide what the highest-leverage system is, then you build it.

Nscale sells the infrastructure that the agentic era runs on, and we intend to run our GTM the same way. The agentic workflows for research, enrichment, signal routing and competitive intelligence that this team will run on are yours to architect, build and ship from the ground up. You are not improving someone else's system; you are creating the one every future hire inherits.

What you'll own:

The commercial source of truth

Implement and structure the CRM from the ground up: account and opportunity data models, pipeline stages, required fields, routing, process hygiene and reporting — one trusted record for every deal and every tranche of capacity
Architect and build the GTM stack from scratch with integrations and automations that eliminate manual work before it ever becomes habit
Own field governance and data integrity with Revenue Enablement — the standard is enforced in the system, not in reminders

The data layer and reporting

Create the data layer the GTM operating rhythm will run on — pipeline health, deal velocity, forecast accuracy, ASP by SKU, utilization, capacity sold-forward. None of this data exists today; you define it, instrument it and make it trustworthy
Build the first reporting these forums will ever run on — the weekly pipeline review, the capacity allocation forum and the WPR are being stood up in parallel with this role
Build executive dashboards that make funnel performance, pipeline coverage and launch adoption legible at a glance

AI-native workflows and automation

Build and ship the first agentic and automated workflows for research, enrichment, account intelligence, signal capture and routing — removing repetitive work while preserving human judgment on strategic accounts
Build the internal tools, dashboards and agents that become this team's core infrastructure — a competitive intelligence hub and rep-facing agents are on the blueprint; you build the first versions
Build the automation behind deal capture and commercial document assembly from zero, as deal desk standardizes its structures

Ecosystem and signal mining

Mine Nscale's unique data — the Ray and Anyscale ecosystem, open-source community activity, platform usage telemetry — to identify high-value prospects, warm paths and expansion signals before competitors see them
Turn usage and adoption signals into sales triggers: who is scaling, who is about to hit capacity, who went quiet

Seller workflows and adoption

Design the first selling workflows with the sellers — there is no playbook to inherit; what you build with them becomes the playbook
Build the onboarding path for every seller who follows: how new hires learn the stack, with Revenue Enablement

You might be a good fit if you have:

4–8 years in revenue operations, sales operations, or GTM engineering at a B2B technology company — ideally including an early-stage or high-growth environment
Hands-on experience implementing and administering a CRM (Salesforce, HubSpot or similar): data models, pipeline design, routing, hygiene and reporting
Strong ability to work with data, APIs, integrations, enrichment sources, automation tools and lightweight scripting (SQL plus Python or similar) to turn fragmented commercial information into usable workflows
The blank-sheet muscle: you can take an ambiguous problem, identify the highest-leverage system to build, and ship it without waiting for detailed requirements
A working understanding of how an early-stage sales org operates — ICP definition, prospect research, pipeline management, handoffs — and a sharp sense for the difference between useful process and unnecessary process
Comfort working directly with sellers, commercial leadership, product, engineering and finance, and translating commercial needs into clear system and data requirements

Strong candidates may also have:

Built 0-to-1 GTM infrastructure before the company had a mature CRM, documented ICP or standardized sales process
Experience with AI-native GTM workflows: research automation, enrichment, account intelligence, workflow orchestration, agent building
Experience at an infrastructure, developer-tools, open-source or consumption-priced company — familiarity with GPUs, Ray or Kubernetes is a plus, not a requirement
Mined community or product-usage data as a source of commercial intelligence
Exposure to quota reporting, attainment tracking or sales compensation operations (a growing part of this role's scope over time)

Why this role, now: Every system this role builds becomes the central nervous system of Nscale's revenue organization — there is no legacy stack to maintain, no process to inherit, and no committee between you and shipping. You will see your work in the Monday pipeline review within weeks of building it.

The range below reflects the base salary for the position. Actual compensation may vary based on job-related factors such as skill set, experience, education, and location. In addition to base salary, this role may be eligible for bonus, equity, and/or commission programs. Nscale may offer a competitive benefits package including medical, dental, vision, flexible paid time off, parental leave, and retirement plan participation.

Salary Range

$120,000—$200,000 USD

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Nscale does not accept unsolicited candidate submissions from recruitment agencies.

Salary Range 120000 - 200000 USD

About the Company

Nscale

Develops and operates AI data centers

Nscale provides end-to-end AI infrastructure by designing, owning, and operating data centers, GPU compute, networking, storage, and managed AI cloud services for enterprises, governments, and AI developers. It offers a full stack from colocated data centers to high-performance GPUs and networking, plus managed cloud services, so customers can run AI workloads efficiently with sovereign European deployments powered by renewables and US campus expansions. Unlike many peers, Nscale combines complete infrastructure ownership with a focus on sovereign, renewable-powered sites and strong capital partnerships to support large-scale, secure AI workloads. Its goal is to become a leading, scalable AI infrastructure provider that enables governments, enterprises, and AI builders to deploy and run AI applications quickly and securely on sovereign data centers with global capacity.

Company size: 1,001-5,000
Company stage: Debt Financing
Total funding: $9.8B
Headquarters: London, United Kingdom
Founded: 2024

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