At a Glance
- Tasks: Experiment with AI assistants and develop innovative legal automation solutions.
- Company: Join one of Europe’s fastest growing startups in the AI field.
- Benefits: Enjoy 33 days off, work remotely, and receive meaningful equity.
- Other info: Dynamic team culture with regular socials and excellent career growth opportunities.
- Why this job: Bridge cutting-edge AI research with real-world applications and make a difference.
- Qualifications: Strong AI research background and proficiency in Python and LLM technologies.
The predicted salary is between 60000 - 80000 £ per year.
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!
Applied Research Engineer employer: jobr.pro
Join a dynamic and innovative team as an Applied Research Engineer, where you'll have the opportunity to work at the forefront of AI technology in a collaborative and supportive environment. With meaningful early-stage equity, generous annual leave, and the flexibility to work from anywhere for a month, we prioritise employee well-being and growth. Our culture fosters creativity and teamwork, ensuring that you can thrive while contributing to groundbreaking legal solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Research Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and legal tech space on LinkedIn or at industry events. We can’t stress enough how personal connections can open doors that applications alone can’t.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs and AI-driven solutions. We love seeing practical examples of your work, so make it easy for us to see what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on the latest trends in AI and machine learning. We want to see that you’re not just knowledgeable but also passionate about the field. Bring your A-game!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love candidates who take the initiative to connect directly with us.
We think you need these skills to ace Applied Research Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your background in AI research, machine learning, and NLP to show us you’re the right fit for the Applied Research Engineer role.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and how your experience aligns with our mission at StudySmarter. Share specific examples of your work with LLMs or any relevant projects that showcase your expertise.
Showcase Your Projects:If you've worked on any prototypes or experimental models, don’t hesitate to include them in your application. We love seeing hands-on experience, especially if it relates to automating workflows or developing AI-driven solutions.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do!
How to prepare for a job interview at jobr.pro
✨Know Your AI Stuff
Make sure you brush up on the latest advancements in AI research, especially around LLMs and multi-agent systems. Be ready to discuss your hands-on experience with model adaptation and fine-tuning, as well as any projects you've worked on that relate to legal workflows.
✨Show Off Your Prototyping Skills
Prepare to talk about any prototypes or experimental models you've developed. Highlight how you approached problem-solving and what techniques you used, like retrieval-augmented generation or embedding strategies. Real-world examples will make you stand out!
✨Get Technical with Python
Since proficiency in Python is key, be ready to discuss your experience with frameworks like FastAPI and Pydantic. You might even want to prepare a few coding examples or scenarios where you optimised LLM inference or worked with APIs.
✨Collaborate and Communicate
This role involves working closely with engineers to transition models into production. Think of examples where you've successfully collaborated on projects, and be prepared to discuss how you communicate complex ideas clearly to non-technical team members.
