Applied Research Engineer
Resumen del puesto por JobGrid
Applied Research Engineer at lawhive: London, Reino Unido; Presencial. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: London, Reino Unido, Presencial
- Source freshness: checked by JobGrid on 2026-05-30.
- Application path: candidates continue to the employer application page with non-personal referral tags.
About Lawhive
Our mission is to make the law accessible to everyone.
The legal industry is built on technology and processes that haven’t been updated in hundreds of years - that's why we've reinvented the entire model from the ground up with our own bespoke AI operating system at the core.
Lawhive is a regulated law firm with an AI-native platform built to amplify expertise and revolutionise the way people practice law, leading to exceptional outcomes for clients and lawyers.
Lawhive Labs is how we bring this vision to life. It's our frontier lab that combines top engineering, design, AI and legal talent from around the world, joining forces to build the future of law.
We’re backed by top-tier investors, including Google Ventures, Balderton Capital and TQ Ventures, and in early 2026, we secured $60M Series B funding round to facilitate international expansion and to grow our team.
We’re headquartered in London and in 2025 successfully launched in the US…and we’re just getting started.
The Role
We’re looking for a Research Engineer to experiment with, develop, and refine LLM-based AI assistants, document automation systems, and case workflow optimisations. This is an opportunity to bridge cutting-edge AI research and real-world applications.
Responsibilities
Conduct applied research on LLM-based reasoning, multi-agent systems and develop frontier bespoke models for automating legal workflows.
Develop prototypes and experimental models to explore novel AI-driven legal solutions.
Design and implement retrieval-augmented generation (RAG) pipelines, leveraging embeddings, vector databases, and structured retrieval techniques.
Optimise LLM inference and fine-tuning using techniques such as LoRA, PEFT, prompt engineering, and caching.
Integrate multi-modal and external knowledge sources to enhance AI-driven insights.
Research and implement autonomous agentic AI systems for complex, multi-step legal workflows.
Stay up to date with the latest advancements in model architectures, alignment and interpretability, and orchestrating complex multi-agent systems.
Collaborate with engineers to transition experimental models into production-ready systems.
Requirements
Strong background in AI research, applied machine learning, and NLP.
Experience with LLM model adaptation, fine-tuning, and inference optimization.
Proficiency in Python, Pydantic, FastAPI, and working with LLM APIs (OpenAI, Anthropic, Mistral, etc.).
Understanding of retrieval-augmented generation (RAG), vector databases, embeddings, and structured AI retrieval.
Hands-on experience with LLM-based planning, reasoning, and autonomous task execution.
Familiarity with self-supervised learning, reinforcement learning, or adaptive AI techniques.
Ability to translate academic AI research into practical experiments and working prototypes.
Experience deploying AI models in cloud environments such as AWS/GCP.
MSc or PhD in AI, ML, Computer Science, or a related field.
UK Benefits:
💰 Meaningful early-stage equity at one of Europe’s fastest growing startups
✈️ 33 days’ annual leave (25 + bank holidays) plus your birthday off
🌍 Work from anywhere for a month
💰 Pension contribution via Nest
💻 Top-spec equipment - MacBook/Windows
⛳️ Regular team building activities and socials!
Diversity at Lawhive
At Lawhive, we know that diversity of thought is critical to delivering outlier outcomes. As such, we’re always working hard to ensure we build a diverse, inclusive team, and as we scale, we’ll only ever increase the focus we apply to this.