LLM Research Engineer

Why join us?

• Applied research on a genuinely unsolved problem, measured against published baselines rather than demos.
• Build systems that explain themselves. Every decision has to carry its evidence and the rule behind it.
• Learn from an ex-Google applied machine learning engineer and from practising clinicians.
• Your results change how insurers handle real claims.

What you'll work on

• Research how large language models can read a patient record and assign standard medical classification codes by applying the official coding rules, rather than pattern-matching from training data.
• Build retrieval over versioned rulebooks so the system reasons from current guideline text, and measure whether that grounding changes the answer.
• Design evidence-linked outputs, where every code carries the source sentence and the rule that justifies it, and a deterministic checker rejects anything unsupported.
• Build benchmarks and locked holdout sets, and measure accuracy against published machine learning results and human specialist baselines.
• Design escalation logic that flags cases where the evidence or the rule is unclear, instead of guessing.
• Run error analysis with clinical reviewers, and write up methods and results so another engineer can reproduce them.

Requirements

• Bachelor's degree in a science, technology, engineering or mathematics discipline.
• Solid programming ability in Python or TypeScript.
• Working knowledge of machine learning or natural language processing fundamentals, from coursework, research or personal projects.
• Comfort with experiments. You can form a hypothesis, design a measurement, and read the result honestly.
• Patience with detail. Much of this work is careful reading of structured rules and taxonomies.
• Legally permitted to work in Hong Kong.

Nice to have

• Projects or coursework in natural language processing, information retrieval or retrieval-augmented generation.
• Experience evaluating LLM systems, including building test sets and scoring rubrics.
• Exposure to healthcare, clinical or insurance data, or to any formal classification system.
• Reading knowledge of Chinese or another second language.

What makes us different

• A real research question with real data, at a company small enough that your result ships.
• Direct mentoring from the technical founders on experiment design and method, plus clinical review of your outputs.
• Based at Hong Kong Science Park, inside the wider deep-tech community.

Location

On-site at Hong Kong Science Park, Pak Shek Kok, New Territories.

Interested?

Send your CV and a short note about a technical problem you have investigated to [email protected]