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Founding ML Engineer — AI Safety & Optimisation

London · In-office £120k–£170k · Meaningful equity

Retained search on behalf of a confidential client - details shared on intro call

About the Client

We're working with an early-stage, well-funded AI startup (backed by top-tier VCs) building systems that need to understand and control how complex, large-scale AI behaviour plays out in the real world - before it goes wrong. The work sits right at the intersection of ML performance and safety: models need to be capable, but also predictable, aligned, and robust once they're live in front of real customers.

Small team, high ownership, direct access to founders. This is a founding/early hire, not a cog-in-a-machine role.

The Role

The core of this role is RL, fine-tuning, and reward design -with safety as the design constraint, not an afterthought. You'll take training methods and turn them into systems that are fast and effective, but also well-behaved: models that stay within intended bounds, resist drift, and fail safely rather than silently.

You'll own the loop end-to-end: reward/training design, post-training and distillation, and production optimisation - all with an eye on catching and correcting unwanted behaviour before it reaches a customer.

What You'll Do

• Design reward functions and training setups that optimise for capability and safe, predictable behaviour
• Post-train, fine-tune, and distil models with alignment and robustness front of mind
• Build evaluation and monitoring approaches that catch drift, edge cases, and failure modes early
• Optimise inference for scale without compromising on safety guardrails — sub-second, high-volume, production-grade
• Build the data pipelines that feed training, evaluation, and safety testing
• Take a method from prototype to production, simplifying aggressively while preserving the safety properties that matter

What We're Looking For

• 2+ years shipping ML in a startup environment, ideally with end-to-end ownership
• Strong hands-on experience with RL, fine-tuning, and/or distillation — bonus points if you've thought hard about reward hacking, alignment, or failure modes
• Excellent Python engineering — clean, maintainable, production-grade
• Comfortable with real-time/low-latency inference systems
• Fluent with statistics, probability, and high-dimensional reasoning
• A genuine interest in safety-conscious ML, not just raw performance chasing
• Fast, high-bar, ownership mentality

Nice to have: distributed training experience, distilling frontier models into small open-weights models for production, background in anomaly/behavioural detection, interpretability or evals work, familiarity with cloud infra (AWS/GCP).

And a quick note: if you're reading this and tick maybe 60% of the boxes above, please still get in touch. The best hires I've made rarely matched every bullet on paper - don't rule yourself out