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- Tasks: Build and deploy scalable ML models that make a real-world impact.
- Company: Fast-growing tech company transforming hospitality operations.
- Benefits: Private medical insurance, equity, gym membership, and a dog-friendly office.
- Why this job: Join a dynamic team and work on innovative ML applications.
- Qualifications: Strong maths and statistics background with ML systems experience.
- Other info: Collaborative culture with regular team meals and excellent growth opportunities.
The predicted salary is between 60000 - 80000 £ per year.
We’re partnering with a fast-growing, well-funded tech company transforming how hospitality businesses staff, plan, and operate in real time — and they’re looking for a Machine Learning Engineer to join their team. This is a hands-on, high-impact role where you’ll take models from idea → production → real-world impact across thousands of sites. You’ll be working on everything from demand forecasting to data pipelines and production deployment in a high-traffic environment used by major UK brands. This isn't your typical Machine Learning role focussed on LLMs. They are looking for talented engineers with a quantitative background and experience with statistical models.
What you’ll be doing:
- Building and deploying scalable ML models powering mobile apps and internal tools
- Designing data architecture for efficient storage, processing, and retrieval
- Developing forecasting and optimisation models for staffing and operations
- Owning ETL pipelines integrating multi-source data (APIs, internal systems)
- Collaborating with data scientists, engineers, and business stakeholders
- Monitoring, maintaining, and improving production ML systems
What they’re looking for:
- Strong foundations in maths, statistics, and modelling
- Experience building production-grade ML systems (e.g. forecasting, optimisation, CV)
- Solid ML Ops and data engineering experience
- Proficiency in Python & SQL (Airflow, PyTorch, Spark a plus)
- Experience with AWS (SageMaker, Lambda, etc.)
- Interest in backend development (Django or Go)
- High level STEM Degree from a leading university.
Nice to have:
- Experience with data lakes / warehousing
- Familiarity with CI/CD, data versioning, and data quality practices
What’s on offer:
- Private medical insurance
- Equity in a high-growth startup
- Gym membership
- Dog-friendly office
- Regular team meals, snacks, and a collaborative, social culture
If you’re excited by real-world ML applications, enjoy building end-to-end systems, and want to make a tangible impact — we’d love to hear from you.👉 Apply now or message me directly for more details.
Machine Learning Engineer in City of London employer: SearchWorks
Contact Detail:
SearchWorks Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at the company. A personal introduction can make all the difference in getting your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that demonstrate your experience with production-grade systems. This will give you an edge and show potential employers what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your maths and statistics knowledge. Be ready to discuss your past projects and how you've tackled challenges in building and deploying ML models. Practice makes perfect!
✨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 hearing from passionate candidates who are excited about making a real-world impact with their skills.
We think you need these skills to ace Machine Learning Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML models, data pipelines, and any relevant projects that showcase your skills in Python and SQL. We want to see how you can make an impact!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how your background aligns with our needs. Be genuine and let your passion for machine learning come through.
Showcase Your Projects: If you've worked on any cool ML projects, don’t forget to mention them! Whether it's demand forecasting or building scalable models, we love seeing real-world applications of your skills. Include links if possible!
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’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at SearchWorks
✨Know Your Models Inside Out
Make sure you can discuss the ML models you've built in detail. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This shows not only your technical skills but also your problem-solving abilities.
✨Brush Up on Your Maths and Stats
Since this role requires a strong foundation in maths and statistics, review key concepts and be prepared to answer questions that test your understanding. You might even get asked to solve a problem on the spot, so practice explaining your reasoning clearly.
✨Familiarise Yourself with Their Tech Stack
Research the tools and technologies mentioned in the job description, like Python, SQL, and AWS. If you have experience with Airflow or PyTorch, be ready to discuss specific projects where you used these tools. Showing familiarity with their tech stack will give you an edge.
✨Prepare for Collaborative Questions
This role involves working closely with data scientists and engineers, so expect questions about teamwork and collaboration. Think of examples from your past experiences where you successfully worked in a team to achieve a common goal, and be ready to share those stories.