Senior Founding Engineer
Intellectual Capital Resources
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 doDepending on your background, you will lead initiatives across several of the following areas:
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Error Analysis & Diagnostics: Investigate subtle failure modes, such as over-fitting to narrow contexts, outdated assumptions, and sub-optimal retrieval or conditioning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
If this role sounds interesting to you, please reach out to Charles Duran.
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 doDepending on your background, you will lead initiatives across several of the following areas:
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
If this role sounds interesting to you, please reach out to Charles Duran.
Reference: WJ-5641_200284 · Posted 13 Nov 2026, 11:25 PM