Founding GTM Operator

Most AI systems stop at insights .
We build systems that act .

We're building the causal backbone for mission-critical digital funnels. A foundation that powers autonomous loops which detect, diagnose, and stop revenue leaks, in production, where mistakes have real consequences.

Actively hiring

Why Augmeta

We're tackling complex problems at the core of modern businesses. As a founding engineer, you won't just ship features. You'll shape the systems, the architecture, and the product direction in a way that lasts.

We're backed and advised by people who've built enduring technology companies. You'll work with a small, senior team that values craft, ownership, and a pace that only happens early on.

We're already working with large enterprises where what we build truly matters. As a founding team member, your work sits close to customers and revenue, and the impact is visible immediately.

The Challenge

How does the system decide whether a KPI movement is real, explainable noise, or an urgent problem?

How do you make agent behavior reliable when the underlying data is incomplete or contradictory?

How do you evaluate quality when there is no obvious ground truth?

How do you build enough trust for the system to move from analysis into action?

How do you let the system improve from history and outcomes without becoming brittle or unsafe?

If these questions sound exciting rather than uncomfortable, you might be a strong fit .

With real upside

Ownership that matters

Medical, dental, vision

When you need it

Health & wellbeing

Fertility benefits included

2 roles open

• Build and ship agentic systems that reason across fragmented enterprise data and workflows
• Design retrieval, memory, and context systems that help the product make better decisions over time
• Build the infrastructure around the model: orchestration, safeguards, fallbacks, logging, replayability, and monitoring
• Build the evaluation infrastructure that measures quality, regressions, trust, and real-world performance — and the feedback loops that use it to improve the system
• Turn inconsistent signals, business logic, and historical outcomes into durable system context
• Design systems that know when to act, when to ask, when to wait, and when to do nothing
• Improve reliability under real production constraints like latency, cost, partial failures, and changing data
• Work across the stack when needed, including backend systems, product surfaces, internal tools, and data flows
• Partner closely with the founders on product direction, technical architecture, and engineering culture

• Have 5+ years building production systems in one or more of: distributed backends, data pipelines, retrieval or agent runtimes, or core ML infrastructure
• Have personally owned systems end-to-end — through design, ship, real failure modes, and recovery — that real users relied on
• Are comfortable moving across backend, data, and AI application layers
• Care deeply about correctness, edge cases, failure modes, and decision quality
• Like ambiguous problems where the right abstraction is not obvious at the start
• Move fast while keeping quality high
• Want real ownership in a small, intense, high-trust team
• Have strong product instincts and like building things people actually use

Strong signal:
You've designed and shipped LLM systems end-to-end — the agents, retrieval, orchestration, and the evals you built to keep them honest — and you understand their failure modes from operating them in production, not from running someone else's benchmark.

• You want a narrowly scoped role with clean boundaries
• You need complete specs before you can start
• You mainly want to do research rather than ship product
• You are excited by demos more than reliability
• You prefer polished environments over messy, early-stage ones
• You optimize for local elegance over end-to-end outcomes

• You will be one of the first…