Skillsearch
Head of Machine Learning
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Head of Machine Learning — AI-native productivity, stealth
Fully remote, UK-based candidates. London HQ likely but not yet fixed.
TL;DR
- Founding ML leadership role at a well-funded stealth AI company
- Product: AI-native productivity — starting with email, expanding into notes, tasks, calendar
- Own the research direction: reasoning, memory, planning, evaluation, alignment
- US$100M initial funding, internally backed, no VC pressure
- Cash + meaningful founding-team equity
- Fully 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 in the corner, a summary at the top of the thread. The company is building the opposite: the proactive layer underneath, that reads context, runs long workflows, completes real tasks, and asks before it acts. First product is an AI-native email app. Cut the four hours a day the average knowledge worker spends in their inbox down to thirty minutes. Email first. Productivity suite next.
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The role, first 12 months
- Own how the system reasons, evaluates, and improves.
- Set the research direction across context representation, memory, planning, and orchestration.
- Decide when to build new architectures versus adapt frontier open-source and commercial models.
- Define evaluation frameworks that measure real-world usefulness, not benchmark vanity.
- Own alignment, safety, and guardrails as product concerns, not afterthoughts.
- Guide exploration of retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems.
- Set the technical bar for research rigor and taste across the org.
The bar
Read this before you DM.
- 8+ years building production ML systems, with real ownership of model behaviour in the wild
- Deep hands-on experience across training, fine-tuning, evaluation, and inference at scale
- Track record of shipping ML into products used by millions, not just papers or prototypes
- Fluent in the current frontier: LLMs, agents, RAG, alignment, evals — you don't need me to define these
- Strong opinions on when to build versus when to leverage what already exists
- Genuinely energised by early-stage ambiguity
Get help with your application
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If you're primarily interested in publishing, chasing benchmarks, or running a large research org, this one probably isn't the right fit.
Who this isn't for
- Pure research managers.
- First-time leaders.
- Anyone who needs a stable spec and a well-defined problem before they can start.
- This is founder-mode ML leadership, not a lab.
The rest
Everything else — who they are, who's behind it, the detail on comp and equity — is a call.
DM me if this is you, or if you know the person it should be.
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