Member of Technical Staff (Applied AI)

About This Opportunity

We are representing a confidential AI research organization based in London that builds specialized evaluation infrastructure for frontier AI systems operating in regulated domains. Our client works at the intersection of machine learning safety and real-world application, creating the training environments and verification systems that help models operate reliably in high-stakes industries.

As a Member of Technical Staff focused on Applied AI, you'll design and build the reinforcement learning environments and evaluation frameworks that AI labs use to train and measure their systems. This is hands-on technical work that bridges engineering, research, and domain expertise—constructing ground truth datasets, writing reward functions, and translating complex professional workflows into structured training tasks.

Responsibilities

• Build reinforcement learning environments across finance, healthcare, and legal use cases
• Design evaluation tasks with verified golden answers, calibrated rubrics, and programmatic reward signals
• Write verification logic and reward functions that distinguish high-quality model outputs from incorrect ones
• Collaborate directly with domain experts (analysts, clinicians, attorneys) to translate professional workflows into structured AI training tasks
• Prototype novel approaches to evaluation, verification, and synthetic data generation
• Own problems end-to-end from initial scoping through production deployment

What We're Looking For

• Practical, hands-on experience building with large language models—prompting, evaluation frameworks, and agentic systems that actually run in production
• High agency and technical depth: you identify what needs doing, figure out the approach, and ship working solutions without waiting for detailed specifications
• Comfort operating across different contexts—you'll move between engineering implementation, evaluation design, and deep collaboration with domain experts, often in the same day
• Strong bias toward shipping and iterating rather than prolonged planning cycles
• Ability to own ambiguous problems from conception through delivery, working directly with experts to scope what "correct" looks like in complex domains
• Active use of LLMs as part of your own development workflow

Nice to have: Domain knowledge in finance, healthcare, or legal; familiarity with reinforcement learning concepts and model training workflows; cloud infrastructure experience (AWS or GCP); previous startup experience as an early engineer.

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team will conduct an initial screening to understand your background and technical approach. We then submit qualified candidates to our client for their interview process. Please do not attempt to contact the client directly—all communication flows through our recruiting team to ensure a structured and confidential process.

Pay

Compensation details will be discussed during the screening process based on experience level and fit.

Get A Job.ai is an equal opportunity recruiter. We work with clients and candidates without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.