Summary
✨ AI‑Generated
Join a stealth, well-funded AI startup as a founding-team Staff Machine Learning Engineer. You will own the full model lifecycle, from data and training through evaluation, inference, and deployment, while helping build an AI-native productivity platform designed to proactively execute complex workflows. The position is fully remote, with UK-based candidates preferred, and includes cash compensation plus meaningful founding-team equity.
Highlights
Founding-team Staff ML role with full ownership of the model lifecycle, fully remote work, cash compensation plus meaningful equity, and strong financial backing without traditional VC pressure.
Description
Staff Machine Learning Engineer — AI-native productivity, stealth
Fully remote, UK-based candidates preferred.
TL;DR
Founding-team Staff MLE at a well-funded stealth AI companyProduct: AI-native productivity — starting with email, expanding into notes, tasks, calendarOwn the full model lifecycle: data, training, evaluation, inference, deploymentUS$100M initial funding, internally backed, no VC pressureCash + meaningful founding-team equityFully remote
The play
Email, calendar, notes, tasks.
The tools 5 billion people run their lives on.
None of them are AI-native.
Every attempt so far has been a bolt-on — a copilot button, a summary at the top of the thread.
This company is building the layer underneath: proactive, context-aware, capable of running long workflows, completing real tasks, and asking before it acts.
First product is AI-native email.
The goal: cut four hours a day in the inbox down to thirty minutes.
Email first.
Productivity suite next.
The role, first 12 months
Own the execution layer of the company's intelligence — turning research and model capabilities into reliable, scalable production systems.
Build and evolve fine-tuning pipelines for large models.
Design evaluation systems that measure real-world capability, robustness, and safety — not benchmark vanity.
Architect high-performance inference infrastructure: latency, GPU utilisation, memory, cost.
Build data pipelines for high-quality real-world and synthetic training data.
Bridge research and application engineering so model improvements actually reach users.
The bar
Read this before you DM.
Production ML systems you've built and shipped — not prototypes, not research demos, real products with real usersDeep understanding of large-model training, fine-tuning, evaluation, and inferenceExperience running GPU-based ML workloads at meaningful scaleStrong software engineering fundamentals — you write production-grade code and you care about correctnessComfortable reasoning about failure modes, model degradation, and what happens when things go wrong in the wildIndependent judgment — you can navigate ambiguity and make pragmatic trade-offs without being hand-held
Who this isn't for
ML engineers who've only ever worked in notebooks.
Researchers chasing publications.
Anyone who needs a stable, well-defined system before they can contribute.
This is a hands-on, high-ownership role in a pre-launch team moving fast.
If the bar above doesn't quite match where you are today — no worries.
Save your energy for the role that does.
The rest
Everything else — who they are, who's behind it, comp and equity detail — is a call.
DM me if this is you, or if you know the person it should be.
