SageOx

Hiring outlook

Very High. $15M seed with an explicitly small team. Ajit stated publicly they will make "a small number of key hires" post-raise. No public roles. No ATS. The only door is founders@sageox.ai or hello@sageox.ai.

Working at SageOx

SageOx: Shared structured team memory for human-agent teams. Founded , currently employees. At this stage, expect opportunity to shape your role based on the company stage.

Most SageOx jobs are based in Seattle.

Why applying the normal way doesn't work

SageOx has an ATS but the real pipeline is referrals → sourced → recruiter picks → cold apps. You can still get through, but know where you stand.

Who to contact at SageOx

What to show them

The core technical challenge at SageOx is building a memory layer that is structured, fast, and comprehension-aware rather than just a vector store. Someone who has worked on knowledge graphs, structured memory for agents, context retrieval systems, or the hard engineering of making AI memory queryable and reliable at production scale is the precise fit. Ryan Snodgrass's distributed systems background and Ajit's XetHub data infrastructure work (building git-scale storage with chunking and deduplication) both point to infrastructure-first thinking. They will hire someone at the same level. Core skills: Distributed systems, structured knowledge representation, LLM context management, vector and graph databases, Python, production ML infrastructure, agent framework internals (LangGraph, LlamaIndex, custom), evaluation design for memory retrieval. Proof of work: Build a minimal shared context layer for a two-agent system where Agent A records a decision with its reasoning, and Agent B consults it before acting on a conflicting input. The interesting engineering problem is the retrieval design: how does Agent B know to check and what it actually gets back. Publish the code and write a README explaining the retrieval mechanism and its failure modes at scale.

A cold email that works at SageOx

What SageOx screens for

Canaan is the most important signal. They are one of the oldest venture firms in the US (founded 1987) and have backed PayPal, ZoomInfo, LivePerson, LendingClub, and Lending Tree. GP Maha Ibrahim is tagged directly in Ajit's LinkedIn announcement post, which is not a coincidence. Canaan typically backs companies they have been tracking for months before the check is written. Their presence signals the thesis has been rigorously stress-tested. At Canaan's check size, the expectation is that SageOx will build the canonical infrastructure layer for the agentic coordination market, not just a useful tool for AI-native teams. That scale ambition shapes what they hire for: people who can build infrastructure that other developers build on, not just end-user products. Pioneer Square Labs is Seattle-native and backed Read.AI, which also operates in the AI team coordination space. That portfolio overlap is not a conflict. It is a signal that PSL has conviction in the coordination infrastructure thesis and is backing both the meeting intelligence layer (Read.AI) and the team memory layer (SageOx) simultaneously. Founders' Co-op, led by Chris DeVore, was the very first investor in Remitly, the company that both Milkana Brace and Galex Yen came from. That relationship is explicit: Matt Oppenheimer publicly congratulated Galex when SageOx was announced, naming the Remitly connection directly. Founders' Co-op's check is a community bet as much as a financial one. Ajit has been explicit about the team philosophy: intentionally small, agent-heavy. "A small number of key hires" means every role is a high-leverage, high-bar addition. The people hired in the next 90 days will define the architecture of the product. Contact: founders@sageox.ai · hello@sageox.ai · @TheSageOx on

Don't send a generic CV to SageOx. Mirror the job posting's language to get past automated screening.

Mistakes that kill SageOx applications

SageOx take-home assignment

SageOx includes a take-home exercise in their interview process. For AI roles, this typically involves a practical problem that takes 2-4 hours. Focus on clean, working code over premature optimization. They're evaluating how you think and communicate, not just the solution.

SageOx interview timeline

At days, SageOx's process is about average than typical for AI (14 days at this size).

Interviewed at SageOx?

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SageOx jobs, frequently asked questions