Account Executive

Andromeda Cluster•San Francisco, CA

37d•Hybrid

About The Position

Andromeda is a market and infrastructure platform to buy, sell, and operate compute. We're in the early stages of a multidecade shift from human driven to agent driven work. Demand for AI infrastructure will outstrip what any handful of vertically integrated providers can supply. It's too expensive, too operationally complex, and too politically constrained to consolidate. Supply is fragmenting instead. AI factories of every shape and size are coming online to fill the gap. Our job is to make all of that fragmented compute flow through one platform, delivering reliable capacity to model builders, research labs, and inference providers exactly when they need it. We sit at the center of three forces: Companies that need reliable, high performance compute fast A fragmented global supply of GPUs across hyperscalers, neoclouds, and independent data centers Capital, risk, and operational complexity most teams aren't equipped to manage When we succeed, trillions of dollars of compute flow through Andromeda. In five years, we won't just participate in the AI infrastructure market. We'll shape it. We're an early stage infrastructure company powering large scale training and inference for frontier labs, AI native startups, and enterprises. We're hiring an Account Executive to own and grow relationships with high priority AI native customers: companies scaling past the point where a single provider's fleet can meet their needs. This isn't a role where you learn the market from scratch. We're looking for someone who already has a book of relationships in this space, from prior GPU, cloud infrastructure, or AI native sales roles, and can start converting it into pipeline immediately. You'll operate as a strategic partner to sophisticated technical buyers, not a feature list vendor. The pitch is access to the market and the expertise to navigate it.

Requirements

A relationship network that converts to pipeline fast. Most directly: prior GPU or cloud infrastructure sales experience across neoclouds, hyperscalers, or inference platforms. We'll also go deep on candidates with a strong book in an adjacent technical product sold to the same buyers, such as storage (e.g. WEKA, VAST, Pure), networking, or MLOps and orchestration tooling, where the relationships are with the same founders, infra leads, and ML teams we sell to, even if the product was different.
5+ years of quota carrying closing experience in complex, technical B2B sales, with a track record of exceeding targets. Earlier career candidates considered if the network and technical fluency are there.
Comfort selling to technical buyers. You can hold your own on architecture and infra tradeoffs, not just pricing and terms.
Strong operational rigor: pipeline hygiene, forecast accuracy, clean CRM data.
Bias for action in an ambiguous, fast moving environment. We're building the sales motion as we go, not executing a playbook that already exists.

Nice To Haves

Experience selling AI/ML infrastructure, cloud infrastructure, GPUs, HPC, or developer platforms specifically.
Familiarity with training, fine tuning, and inference workflows for LLMs and generative AI.
Experience navigating compute decisions tied to funding rounds, major training runs, or product launches. You understand why timing matters to these buyers.
Prior zero to one GTM experience at a high growth startup.

Responsibilities

Bring and activate existing relationships with AI native founders, infra leads, and ML teams. This is the fastest path to real pipeline in your first 90 days.
Own the full sales cycle: outbound, first meetings, technical validation, commercial negotiation, close, with real discipline around deal progression and forecast accuracy. We call deals real or we don't call them.
Run a consultative process built on technical credibility. You should be able to talk architecture, training vs. inference economics, and cluster tradeoffs as a peer, not recite a pitch deck.
Partner with Solutions Engineering to run technical evaluations.
Multithread accounts properly: technical, commercial, and executive stakeholders in parallel, not sequentially.
Keep pipeline data clean and current: stakeholders, technical requirements, next steps, forecast.
Track GPU supply and demand trends, model lifecycle needs, and the competitive infra landscape closely enough to bring customers a point of view, not just a quote.
Travel up to 35% for customer and prospect meetings, industry events, and site visits.