Machine Learning Engineer, Safety
harrison clarkeMillbrae (CA)
About the role
Machine Learning Engineer, Safety | Stealth Mode Frontier AI Lab | Bay AreaI'm working with a well-funded, early-stage stealth AI lab building genuinely frontier systems - and they're hiring a Machine Learning Engineer focused on safety.
The mission: make advanced AI systems reliable, controllable, and aligned as their capabilities grow. This is hands-on, unsolved-problem work at the edge of what's possible.
What you'd work onEvaluation and oversight systems for advanced reasoning and agentic behaviour
Red-teaming and adversarial testing - turning findings into real model and training improvements
Safety-focused post-training, reward modelling, and guardrails
Identifying and mitigating failure modes in complex, multi-step reasoning
You might be a fit if you haveStrong ML engineering skills and hands-on experience with LLMs / foundation models
Work in one or more of: post-training (SFT/RL/RLHF), evals, red-teaming, alignment, or safety infrastructure
A bias toward shipping and owning problems end-to-end in an ambiguous environment
Real interest in the hard problems of frontier AI safety
DetailsBay Area, hybrid
Small, senior, talent-dense team - real ownership from day one
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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