Technical Account Manager - AI Infrastructure

Prime Intellect•San Francisco, CA

2d•$160,000 - $200,000•Hybrid

About The Position

Prime Intellect is building the open superintelligence stack — from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts. We recently raised $15M in funding, led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy, Tri Dao, Dylan Patel, Clem Delangue, Emad Mostaque, and many others. Your Role Prime Intellect serves some of the most sophisticated AI teams in the world that depend on our compute and infrastructure to train and deploy production AI systems. The Customer Success Manager is the person who makes sure those customers succeed, scale, and keep building with us. This is not a traditional Customer Success role. Our customers run large-scale training jobs, scale inference workloads against real production traffic, and depend on cluster reliability and performance the way most companies depend on their cloud provider. The work spans the technical and the commercial — you'll be reading Grafana dashboards and discussing cluster performance with a customer's ML infrastructure team in the morning, and partnering with Sales on a capacity expansion in the afternoon. You'll own a portfolio of enterprise customers end-to-end and build the relationships that make Prime Intellect the partner of choice for their AI infrastructure.

Requirements

3–6 years in Customer Success, Technical Account Management, Solutions Engineering, or adjacent roles at infrastructure, cloud, or AI/ML companies
Strong technical fluency — comfortable reading dashboards, discussing infrastructure architecture, and engaging with customer engineering teams without a translator
Strong commercial instincts — you understand that Customer Success is a revenue function, not a support function, and you can drive real expansion alongside technical outcomes
Deep customer empathy combined with high judgment — you advocate for customers internally while making the calls that are right for the business
Excellent verbal and written communication, especially when explaining complex technical issues to non-technical stakeholders and vice versa
High ownership — you see gaps and build the fix before anyone asks
Comfortable in ambiguity and speed; this market doesn't slow down
AI-native in how you work: you use LLMs, automation, and programmatic tools to move faster

Nice To Haves

Direct experience at a cloud provider, AI infrastructure company, or compute marketplace
Familiarity with GPU economics, training and inference workloads, or compute consumption patterns
Background as a TAM or Solutions Architect at a hyperscaler (AWS, GCP, Azure) or specialized cloud provider
Working knowledge of usage-based pricing, capacity commitments, and consumption-based contracts
You've been an early Customer Success hire at a high-growth company

Responsibilities

Own a portfolio of enterprise customers end-to-end — adoption, retention, expansion, and overall health
Build deep relationships with technical and executive stakeholders at each customer, from ML engineers to engineering leadership
Drive customer outcomes: faster time-to-value on first workloads, smooth scaling as their usage grows, and meaningful expansion as their AI ambitions expand
Understand each customer's training and inference workloads at a real technical level — what models they're training, what infrastructure they need, what their performance bottlenecks are
Partner with customers' engineering teams on cluster performance, capacity planning, workload optimization, and migration
Translate customer needs into clear, prioritized feedback for our Engineering and Product teams
Identify expansion opportunities ahead of the customer — anticipate scaling needs, surface new use cases, drive adoption of new products (Lab, Inference, additional compute capacity)
Partner with Sales on renewal conversations and growth motions
Maintain visibility into the economics of each customer relationship, in partnership with Finance and Compute
Serve as the first line for customer-facing operational issues — usage questions, capacity changes, SLA tracking, incident communications
Build the cross-functional connective tissue between Sales, Engineering, Finance, and customers