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- Tasks: Join us to develop cutting-edge AI solutions for legal workflows and enhance user experiences.
- Company: Lawhive is a pioneering legal tech platform on a mission to democratise access to the law.
- Benefits: Enjoy 34 holidays, equity options, a pension, and fun team-building activities!
- Why this job: Be part of a rapidly growing company making a real social impact in the legal field.
- Qualifications: Strong AI research background, proficiency in Python, and experience with LLM models required.
- Other info: Opportunity to work on innovative projects backed by top VC funds and expand into the US market.
The predicted salary is between 48000 - 84000 £ per year.
We’re on a mission to make sure everyone has access to the law. Backed by top VC funds and a recent $40M Series A—one of Europe's five largest Series A raises of 2024—Lawhive is poised for rapid growth and expansion into the US market. As a pioneering legal tech platform, we are seeking a Senior Research Engineer to join our mission of democratising access to the law. This role offers the opportunity to work on cutting-edge AI products, build best-in-class user experiences, and help solve one of society's most pressing problems.
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 developing 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.
Qualifications:
- 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.
Benefits:
- 34 Holidays (25 days annual leave + your birthday off + bank holidays in England)
- Equity (Share Options)
- Pension
- Regular team building activities, socials, and annual retreat!
- 20% off legal fees through Lawhive
Senior AI Research Engineer employer: Sequel
Contact Detail:
Sequel Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI Research Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in AI research, particularly in LLMs and multi-agent systems. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Showcase your hands-on experience with relevant technologies like Python, FastAPI, and LLM APIs. Be prepared to discuss specific projects where you've implemented these tools, as practical examples can set you apart from other candidates.
✨Tip Number 3
Network with professionals in the legal tech and AI fields. Attend industry events or webinars to connect with potential colleagues and learn more about the challenges they face, which can give you insights to discuss during your interview.
✨Tip Number 4
Prepare to demonstrate your ability to translate academic research into practical applications. Think of examples where you've taken theoretical concepts and developed them into working prototypes, as this aligns perfectly with the role's requirements.
We think you need these skills to ace Senior AI Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in AI research, machine learning, and NLP. Focus on relevant projects, especially those involving LLMs and legal tech, to demonstrate your fit for the role.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for democratizing access to law through technology. Mention specific experiences that align with the responsibilities listed in the job description, such as developing AI-driven legal solutions.
Showcase Technical Skills: In your application, emphasise your proficiency in Python, FastAPI, and working with LLM APIs. Provide examples of how you've used these skills in past projects, particularly in cloud environments like AWS or GCP.
Highlight Research Experience: Detail any applied research you've conducted, especially related to LLM-based reasoning and multi-agent systems. Discuss how your research has led to practical applications or prototypes, which is crucial for this role.
How to prepare for a job interview at Sequel
✨Showcase Your AI Knowledge
Make sure to highlight your understanding of AI research, particularly in LLMs and NLP. Be prepared to discuss specific projects you've worked on that demonstrate your expertise in these areas.
✨Demonstrate Problem-Solving Skills
Prepare examples of how you've tackled complex problems using AI solutions. Discuss your approach to developing prototypes and experimental models, especially in the context of legal workflows.
✨Familiarise Yourself with RAG Techniques
Since the role involves retrieval-augmented generation, brush up on your knowledge of RAG pipelines and vector databases. Be ready to explain how you would implement these techniques in real-world applications.
✨Engage in Technical Discussions
Expect technical questions about model optimisation and fine-tuning. Practice explaining concepts like LoRA, PEFT, and prompt engineering clearly, as this will show your depth of understanding and ability to communicate complex ideas.
