Drive the development of next-generation language models. Work on high-impact, safety-critical AI systems. Build real-world systems capable of deployment at scale.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Job Title: LLM Research Engineer**
Location: Remote-Hybrid (with optional office access)
About the Opportunity
We are representing a fast-growing AI organisation seeking an LLM Research Engineer to drive the development of its next generation language models. The company is working on high-impact, safety-critical AI systems and is expanding its research team to push forward new capabilities in reasoning, alignment and scalable training. This role offers the chance to work on foundational research while building real-world systems capable of deployment at scale.
Key Responsibilities
- Research, prototype and optimise large language models, including new training methods and architectures
- Fine-tune and align LLMs using instruction tuning, RLHF, safety datasets and evaluation frameworks
- Design and experiment with data strategies (filtering, synthetic generation, augmentation, curriculum learning)
- Build scalable training and evaluation pipelines for model experimentation
- Work closely with researchers to transition prototypes into production-grade models
- Contribute to publications, internal research papers and benchmarking initiatives
Candidate Requirements
- Proven experience in LLM research, applied ML research or advanced language model engineering
- Strong proficiency in Python and deep learning frameworks (e.g. PyTorch, JAX, TensorFlow)
- Solid understanding of model training, tokenisation, optimisation, evaluation methods and scaling laws
- Ability to apply research rigor while writing high-quality, maintainable engineering code
- Strong analytical skills, curiosity and ability to collaborate within a research-heavy team
Desirable Experience
- Experience with RLHF, RLAIF, preference modelling, adversarial evaluation or model-safety research
- Familiarity with distributed training, large-scale data curation or synthetic data pipelines
- Contributions to research papers, open-source model development, benchmarks or LLM tooling
- Previous work in research-driven start-ups or labs
Benefits & Culture
- Competitive compensation including equity
- Flexible hybrid working + relocation support if required
- Opportunity to influence early-stage research directions and core model design
- Mission-driven environment focused on advanced, safe and impactful AI innovation
How to Apply
Interested candidates are invited to submit their CV. Full company details and research focus areas will be shared during the recruitment process.