At a Glance
- Tasks: Lead the development of AI-native productivity tools, transforming how people manage their tasks.
- Company: Join a stealth AI company with significant funding and a focus on innovation.
- Benefits: Competitive salary, equity options, and fully remote work flexibility.
- Other info: Fast-paced environment with high ownership and excellent growth potential.
- Why this job: Be part of a founding team creating groundbreaking AI solutions that enhance productivity.
- Qualifications: Experience in building production ML systems and strong software engineering skills required.
The predicted salary is between 63000 - 77000 £ per year.
Fully remote, UK-based candidates preferred.
Founding-team Staff MLE at a well-funded stealth AI company.
Product: AI-native productivity — starting with email, expanding into notes, tasks, calendar.
Own the full model lifecycle: data, training, evaluation, inference, deployment.
US$100M initial funding, internally backed, no VC pressure.
Cash + meaningful founding-team equity.
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:
- Production ML systems you’ve built and shipped — not prototypes, not research demos, real products with real users.
- Deep understanding of large-model training, fine-tuning, evaluation, and inference.
- Experience running GPU-based ML workloads at meaningful scale.
- Strong software engineering fundamentals — you write production-grade code and you care about correctness.
- Comfortable reasoning about failure modes, model degradation, and what happens when things go wrong in the wild.
- Independent 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.
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