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- Tasks: Transform cutting-edge AI research into impactful learning products for thousands of users.
- Company: Join a forward-thinking tech company with a focus on innovation and collaboration.
- Benefits: Enjoy 27 days annual leave, private medical insurance, gym membership, and mental health support.
- Why this job: Make a real difference by developing AI solutions that enhance learning experiences.
- Qualifications: Experience in machine learning, Python, and large language models is essential.
- Other info: Flexible hybrid working model with excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Hybrid working model: three days per week in the office (Monday, Tuesday, plus one flexible day).
27 days of annual leave plus 8 bank holidays and 5 additional days, including life event, volunteering, and company-wide wellbeing days.
Private medical insurance, medical cashback, life insurance, gym membership, wellbeing resources, and access to mental health support.
Opportunity to work remotely from anywhere for up to 10 days per year.
As a Senior Machine Learning/Applied AI Engineer, the successful candidate will turn cutting-edge AI research into real-world products that make learning and development smarter, more personalised, and more impactful for thousands of users.
The Senior Machine Learning/Applied AI Engineer role sits within a growing Applied Science team and is focused on building, deploying, and scaling AI systems that operate in real production environments.
- Design & Deliver AI Solutions: Partner with Product, Design, and Data teams to shape and deliver AI-powered features that drive meaningful impact for learners and create value for customers.
- Leverage Large Language Models (LLMs): Design, fine-tune, and integrate LLM-powered solutions for use cases including content generation, semantic search, summarisation, and personalised learning experiences.
- Build & Integrate Models: Develop, fine-tune, and embed machine learning models into production systems using modern AI tooling, ensuring solutions are scalable, reliable, and performant.
- Own the End-to-End Lifecycle: Take responsibility for the full lifecycle, from raw data and experimentation through deployment, monitoring, and continuous iteration. Track performance, accuracy, and adoption of AI features, using insights to drive continuous improvement.
- Share expertise: Help make AI approachable, enabling teams across the organisation to enhance their work with AI.
- Build robust pipelines: For model training, deployment, monitoring, and retraining using AWS and modern MLOps best practices.
- Champion Innovation: Stay ahead of emerging AI tools and techniques, applying them to deliver exceptional, user-focused experiences.
- Hands-On AI/ML Expertise: Experience building and deploying machine learning models using frameworks such as PyTorch, TensorFlow, or scikit-learn.
- LLM Experience: Proven experience working with large language models.
- Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications.
- Modern AI Tooling: Familiarity with emerging AI development tools such as Cursor and Gemini is highly desirable.
- Practical experience: Deploying AI solutions on AWS, including CI/CD, model versioning, observability, and retraining pipelines.
- Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering.
- Collaborative Approach: Thrives in cross-functional, creative teams and values building together.
Senior Machine Learning Engineer (M/F/D) in City of London employer: Burns Sheehan
Contact Detail:
Burns Sheehan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer (M/F/D) in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, 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 machine learning projects, especially those using LLMs. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest AI trends. Practice explaining complex concepts in simple terms, as this will help you connect with non-technical team members.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Senior Machine Learning Engineer (M/F/D) in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior Machine Learning Engineer role. Highlight your hands-on AI/ML expertise and any experience with large language models, as these are key for us.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how you can contribute to our mission. Share specific examples of projects you've worked on that align with the responsibilities outlined in the job description.
Showcase Your Projects: If you've built or deployed any machine learning models, make sure to include them in your application. We love seeing real-world applications of your skills, so links to GitHub repos or project demos can really make you stand out.
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 shows us you’re keen on joining the StudySmarter team!
How to prepare for a job interview at Burns Sheehan
✨Know Your AI Stuff
Make sure you brush up on the latest trends in AI and machine learning, especially around large language models. Be ready to discuss your hands-on experience with frameworks like PyTorch or TensorFlow, and how you've applied them in real-world scenarios.
✨Showcase Your Collaboration Skills
Since this role involves working closely with Product, Design, and Data teams, be prepared to share examples of how you've successfully collaborated in the past. Highlight any cross-functional projects where you contributed to building AI-powered features.
✨Demonstrate Your Problem-Solving Ability
Think of specific challenges you've faced in deploying machine learning models and how you overcame them. Discuss your approach to monitoring and iterating on AI solutions, as well as how you track performance and accuracy.
✨Be Ready for Technical Questions
Expect some deep dives into your technical expertise, especially around AWS and MLOps best practices. Prepare to explain your experience with model training pipelines, CI/CD processes, and how you ensure scalability and reliability in production environments.