Foundation Model Engineer in Cambridge

Foundation Model Engineer in Cambridge

Cambridge Full-Time 70000 - 90000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Design and implement cutting-edge AI models and optimise data pipelines for performance.
  • Company: Join a non-profit focused on collaborative engineering in AI innovation.
  • Benefits: Competitive salary, pension, professional development, and networking opportunities.
  • Other info: Vibrant office near Cambridge station with a supportive and inclusive culture.
  • 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 70000 - 90000 £ 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
  • Proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow)
  • Experience building and maintaining ML pipelines in production
  • Familiarity with GPU usage and optimisation
  • Ability to debug and improve model performance systematically

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

CommonAI CIC is an exceptional employer, offering a collaborative and supportive work environment where innovative minds come together to drive the responsible development of foundational AI technologies. Located just minutes from Cambridge train station, employees benefit from a vibrant office atmosphere, competitive salary packages, and ample professional development opportunities, all while making a significant impact in a growing organisation dedicated to inclusivity and diversity.

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Contact Details:

Commonai Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Foundation Model Engineer in Cambridge

Get Involved in Data Science Meetups

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Show Off Your Projects

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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Commonai.

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When you find a suitable opening like Foundation Model Engineer at Commonai, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Foundation Model Engineer in Cambridge

Building LLMs
Training Multimodal Models
Data Pipeline Development
Model Evaluation and Validation
Overfitting Management
Feature Engineering
Proficiency in Python

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

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Craft a Tailored Cover Letter:For a full-time role at Commonai, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Commonai. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Commonai

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Commonai!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.