At a Glance
- Tasks: Design and implement cutting-edge AI models and optimise training pipelines.
- Company: Join a non-profit driving responsible AI innovation with a collaborative spirit.
- Benefits: Competitive salary, pension, professional development, and networking opportunities.
- Other info: Vibrant office near Cambridge station with excellent career growth potential.
- Why this job: Make a real impact in AI while working with top industry experts.
- Qualifications: Experience with LLMs, Python, and ML frameworks; strong analytical skills.
The predicted salary is between 60000 - 80000 £ per year.
CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to co-develop and grow businesses, fast.
We are led by experienced founders, investors and engineers who believe that collaborative engineering drives faster AI innovation and are backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems.
We’re seeking a highly skilled foundation model engineer who has experience of building, training, evaluating, and deploying LLMs or multimodal models end-to-end. We are currently building an AI lab with multiple GPU clusters for testing new hardware and software technologies to accelerate machine learning and inference. This exciting role will primarily focus on model development, data pipelines and system performance. You’ll work across the full AI lifecycle, from experimentation to scalable deployment, with a strong emphasis on technical depth and rigour.
What You’ll Do
- Design and implement end-to-end LLM training pipelines
- Source and, where appropriate, preprocess datasets for training and evaluation
- Fine-tune and optimise open weight models (LLMs, vision, or traditional ML)
- Build evaluation frameworks and define performance metrics
- Develop and maintain data pipelines and training workflows
- Analyse training pipelines and optimise them for latency, cost, and scalability
- Implement monitoring, logging, and feedback loops for continuous improvement
- Experiment with modern AI tooling and services to investigate how they can be leveraged
Requirements
- Proven experience training and fine-tuning LLMs or multimodal models (not just using APIs)
- Solid understanding of:
- Model evaluation and validation
- Overfitting, bias/variance tradeoffs
- Data quality and feature engineering
We also value:
- Knowledge of distributed training or large-scale data processing
- Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning)
- Background in applied research or publishing
- Familiarity with retrieval systems, embeddings, or ranking models
Ideally you will have a maths or computer science research background with a focus on developing new algorithms or techniques for training and deploying AI models. You may also have been working in industry in a large organisation or start-up with an emphasis on developing and deploying cutting edge machine learning.
When applying, please include:
- Links to relevant projects, papers, or GitHub repositories
- A brief description of a model/system you trained and deployed end-to-end
Benefits
- A collaborative and supportive work environment
- The opportunity to have a high impact in a growing organisation
- Competitive salary package and pension
- Professional development opportunities
- Networking opportunities with influential people from across the tech sector and academia
- A vibrant office environment located a few minutes’ walk away from Cambridge train station
CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace.
Foundation Model Engineer in Cambridge employer: CommonAI CIC
Contact Detail:
CommonAI CIC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Foundation Model Engineer in Cambridge
✨Tip Number 1
Network like a pro! Reach out to folks in the AI community, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs or multimodal models. Having tangible examples of your work can really set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML frameworks. Practice coding challenges and be ready to discuss your past projects in detail. We want to see your thought process!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our collaborative environment.
We think you need these skills to ace Foundation Model Engineer in Cambridge
Some tips for your application 🫡
Show Off Your Skills: When you're writing your application, make sure to highlight your experience with LLMs and multimodal models. We want to see how you've built, trained, and deployed these systems, so don’t hold back on the details!
Link It Up: Include links to your relevant projects, papers, or GitHub repositories. This gives us a chance to see your work in action and understand your approach to model development and data pipelines.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make sure your descriptions of past projects and experiences are easy to read and understand.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity with CommonAI CIC.
How to prepare for a job interview at CommonAI CIC
✨Know Your Models Inside Out
Make sure you can discuss your experience with LLMs and multimodal models in detail. Be ready to explain the end-to-end processes you've implemented, from training to deployment. This shows not only your technical skills but also your passion for AI.
✨Showcase Your Projects
Bring along links to your GitHub repositories or any relevant projects you've worked on. A brief description of a model or system you've trained and deployed will help illustrate your hands-on experience and problem-solving abilities.
✨Understand the Importance of Data
Be prepared to talk about data quality, feature engineering, and how you've sourced and preprocessed datasets in the past. This is crucial for demonstrating your understanding of the foundational aspects of model training and evaluation.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's AI lab, their current projects, and how they approach collaborative engineering. This not only shows your interest in the role but also helps you gauge if the company aligns with your values and career goals.