Senior Founding Engineer

IC Resources; Greater London; On-site; GBP 90,000 - 160,000; Full time

Job summary

Intellectual Capital Resources Ltd in London, United Kingdom, seeks a Senior ML Research Engineer/Scientist to architect and own model evaluation frameworks and post-training pipelines. You will lead a senior technical group to shape the roadmap and research direction for adaptive intelligence systems.

The role requires 5+ years of production ML/LLM experience, deep knowledge of model post-training, LLM evaluation, and reinforcement learning from feedback.

Qualifications

• Hands-on mastery in model post-training, LLM evaluation, preference data curation, reinforcement learning from feedback, or distillation.
• Strong proficiency in Python and modern deep learning frameworks (PyTorch or JAX).
• Experience leading technical vision and mentoring engineers or researchers.

Responsibilities

• Design and execute post-training workflows—including preference optimisation, reward modelling, reinforcement learning, and distillation.
• Build rigorous evaluation suites to assess model outputs across contexts and histories.
• Architect strategies for curating high-signal training data using human feedback and synthetic trajectories.
• Evaluate and improve context retention, memory, and adaptation over extended interactions.
• Investigate failure modes and improve inference or retrieval conditioning.

Skills

Python

Experiment design

LLM evaluation

Reinforcement learning from feedback

Mentoring

Tools

PyTorch

JAX

Experiment tracking

Job description

Senior ML Research Engineer / Scientist - Model Alignment & Evaluation

About the opportunity

We are working with a well-backed, early-stage AI company building next-generation adaptive intelligence. Operating with a lean, high-calibre team, the company is looking for a senior technical leader to architect and own its model evaluation frameworks and post-training pipelines. You will play a pivotal role in shaping both the core technical roadmap and the long-term research direction.

What you will do

• Model Alignment & Fine-Tuning: Design and execute post-training workflows—including preference optimisation, reward modelling, reinforcement learning, and distillation—to build responsive, context-aware systems
• Dynamic Evaluation Frameworks: Build rigorous evaluation suites capable of assessing nuanced model outputs across varying contexts, constraints, and interaction histories
• Data & Feedback Pipelines: Architect strategies for curating high-signal training data, leveraging human feedback, demonstrations, critiques, and synthetic trajectories
• Context & Memory Systems: Evaluate and improve how models retain, prioritise, and adapt to evolving information over extended interactions
• Error Analysis & Diagnostics: Investigate subtle failure modes, such as over-fitting to narrow contexts, outdated assumptions, and sub-optimal retrieval or conditioning

What we are looking for

Experience: 5+ years of industry experience developing production machine learning or large language model systems, with a proven track record of taking complex initiatives from initial concept to deployment

Technical Depth: Hands‑on mastery in model post-training, LLM evaluation, preference data curation, reinforcement learning from feedback, or distillation

Engineering Excellence: Strong proficiency in Python and modern deep learning frameworks (PyTorch or JAX), backed by solid fundamentals in data engineering, experiment tracking, or inference infrastructure

Research Execution: Demonstrated ability to take open-ended research questions, design clean experiments, and translate findings into scalable systems

Leadership: Experience setting technical vision and mentoring fellow engineers or researchers