Senior Engineer - Backend

The Opportunity

What we're looking for:

We're looking for a generalist, high-agency engineer with 5+ years of backend experience building customer-facing products. You should have startup DNA — experience at top-tier startups (Series B or earlier) or as a founder — and a product mindset that ties technical decisions to business impact. We need someone who questions the "why," drives projects end-to-end without oversight, and thrives in early-stage ambiguity with high ownership and fast iteration.

What you'll do:

Own and ship product features end-to-end — from problem scoping through backend implementation to production

Design and ship LLM agents that reason over large, messy, real-world organizational data, and make them reliable enough to trust

Make key technical decisions across the product and backend — architecture, data modeling, APIs, tooling — and set the engineering quality bar

Operate with full autonomy; in a team this small, your judgment directly shapes the product, the codebase, and the company

Own high-risk, high-impact projects in a fire-and-forget work style with minimal hand-holding

Shape the product direction alongside the founding team; translate customer needs into technical solutions

Work closely with the founding team to support growing client engagements ranging from hundreds to thousands of employees

5+ years of experience with focus on customer-facing products

About the Company

The company is building the AI organizational memory system — a platform that transforms a company's collective knowledge, data, and decisions into living intelligence. As companies scale, they inevitably lose context: decisions get forgotten, institutional knowledge walks out the door, and alignment fractures across teams. The company solves this by creating a persistent memory layer that understands an organization's context, goals, and culture, giving both individual contributors and leaders unprecedented transparency into what's happening across the company and how it all aligns.

Their goal is simple and hard: never let a company lose itself as it scales. The company's value compounds over time, driven by accumulated organizational context rather than dependence on any single model. The platform reasons over large, messy, real-world organizational data using LLM agents — making them reliable enough to trust at enterprise scale.

Traction & Customers The company is already working with enterprise design partners and pilots, including a public company in Southeast Asia with 15,000 employees (1,500 in corporate). They have multiple additional engagements onboarding in the hundreds-to-thousands employee range, with a growing pipeline of enterprise customers.

Backing & Team The company raised a $5M seed round in early 2026, led by a16z Speedrun and Together Fund, with participation from Precursor Ventures, Inovia Capital, Parable VC, Gokul Rajaram, and other notable investors. The team is approximately 17 people, with around 7 full-time engineers, and operates out of offices in San Francisco and Toronto with a remote-friendly culture.

The company works closely with leaders from some of the strongest engineering organizations in the Valley and is building foundational AI infrastructure — not wrapper apps. This is hard, novel work at the intersection of enterprise software, distributed systems, and agentic AI.

Founded in 2025, team size around 10 employees.

About the Team

The company's engineering team is a small, high-caliber group of around 7 full-time engineers building AI organizational memory infrastructure from the ground up. Led by co-founder Andrey Starenky, the team operates with extreme autonomy and ownership — every engineer is expected to independently own high-risk, high-impact projects in a "fire and forget" work style.

The team is distributed across San Francisco and Toronto, with some engineers working remotely (e.g., Seattle) and flying in periodically for in-person collaboration. The culture is fast-paced, iterative, and startup-native — they value senior engineers with founding experience or entrepreneurial backgrounds who can set technical direction without hand-holding.

What defines this team: High agency, high ownership. The team explicitly optimizes for engineers who define problems themselves and drive solutions end-to-end. They've parted ways with junior engineers to raise the bar on seniority and independence.

AI-native builders. Everyone on the team is expected to have a sophisticated relationship with AI — using it as a force multiplier while maintaining strong first-principles thinking. They build LLM agents that reason over large, messy enterprise data.

Methodical problem solvers. The team values diagnosis before prescription — understanding what the actual problem is before jumping to solutions. First-principles thinking is core to how they operate.

Move fast, stay rigorous. With active enterprise deployments (including a 15,000-person public company in Southeast Asia), the team ships production systems at startup speed while maintaining high engineering standards.

The team works closely with leadership and investors from some of the strongest engineering organizations in the Valley. At ~17 people total, every engineer's decisions have immediate, visible impact on the product and the company's trajectory.

The Product

The company is an AI organizational memory system that transforms a company's collective knowledge, data, and decisions into living intelligence. The platform captures and structures the context, goals, and culture of an organization — giving both individual contributors and leaders unprecedented transparency into what's happening across the company and how it all aligns.

The company's LLM agents reason over large, messy, real-world organizational data to ensure companies never lose themselves as they scale. The system's value compounds over time, driven by accumulated organizational context rather than dependence on any single model. The company is actively deployed with enterprise customers, including a public company in Southeast Asia with 15,000 employees, and is onboarding multiple design partners ranging from hundreds to thousands of employees.

Tech Stack

Python, SQL, Distributed Systems, API Design, LLMs, RAG, Agentic Frameworks, Platform Engineering

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Application Questions

1. Walk us through a recent project you shipped end-to-end — what was the problem, how did you approach it, and what did your day-to-day workflow look like?
2. Tell us about something you've built that involves LLMs or AI agents. What were the trickiest technical decisions and how did you work through them?
3. What stage companies are you prioritizing in your search, and why?