At a Glance
- Tasks: Design and build data pipelines, transform ML prototypes into production-ready systems.
- Company: Join a leading tech firm with a focus on innovation and collaboration.
- Benefits: Competitive salary, hybrid work model, generous holiday allowance, wellness facilities.
- Other info: Clear career progression and opportunities to work with cutting-edge technology.
- Why this job: Make a real impact by influencing how millions engage with products daily.
- Qualifications: Degree in relevant field and experience in data or ML engineering.
The predicted salary is between 60000 - 65000 € per year.
We are currently looking for a Machine Learning Engineer to join our client’s data team. This is a hands‑on role where you’ll design and build robust data pipelines, transform ML prototypes into production‑ready systems, and champion MLOps best practices across the business. As a Machine Learning Engineer, you’ll play a crucial role in ensuring our clients’ data and AI strategy scales effectively, directly influencing the way millions of people engage with their products every day.
This is a unique chance to combine data engineering with machine learning in a high‑impact environment. You’ll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable, and production‑ready. Unlike many roles in the tech sector, this Machine Learning Engineer role gives you the visibility of seeing your work applied at scale, powering decision‑making and user experiences for a vast audience.
Your day‑to‑day will include:
- Building and maintaining end‑to‑end data pipelines and feature engineering workflows.
- Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML.
- Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance.
- Supporting the data warehouse and ensuring data quality, governance, and accessibility.
- Collaborating with cross‑functional teams to deliver trusted datasets and insights.
What’s in it for you?
- Competitive salary with annual reviews.
- Hybrid working model offering flexibility.
- Generous holiday allowance that increases with service.
- Onsite wellness facilities, subsidised meals, and gym access.
- Access to wellbeing support services and employee assistance programmes.
- Clear career progression and opportunities to work with cutting‑edge tech.
Skills and Experience:
- Degree in Computer Science, Engineering, Mathematics, or a related field.
- Proven experience in data or ML engineering.
- Strong knowledge of Python and SQL.
- Hands‑on experience with cloud platforms (GCP or Azure) and Databricks.
- Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.
Nice-to-have:
- Experience with Spark, CI/CD pipelines, and orchestration tools.
- Knowledge of Elasticsearch or digital/web analytics platforms.
- Understanding of the full machine learning lifecycle, from experimentation to evaluation.
ML Engineer employer: Data Science Festival
Join a forward-thinking company that values innovation and collaboration, where as a Machine Learning Engineer in London, you'll have the opportunity to work with cutting-edge technology in a hybrid environment. Enjoy a competitive salary, generous holiday allowance, and access to wellness facilities, all while contributing to impactful projects that enhance user experiences for millions. With clear career progression and a supportive work culture, this role offers a meaningful path for professional growth and development.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage with online communities. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and data pipelines. We recommend using platforms like GitHub to share your code and demonstrate your expertise to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and tools. We suggest doing mock interviews with friends or using online resources to practice your responses. The more comfortable you are, the better you’ll perform when it counts!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications this way!
We think you need these skills to ace ML Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with data pipelines, ML models, and any relevant tools like MLflow or Azure ML. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Be sure to mention any specific projects or experiences that relate to the job description.
Showcase Your Projects:If you've worked on any cool ML projects, don’t forget to mention them! Whether it's a personal project or something from your previous job, we love seeing practical applications of your skills. Include links if possible!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at Data Science Festival
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like Python, SQL, and cloud platforms such as GCP or Azure. Brush up on your experience with MLflow, Vertex AI, or Azure ML, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built data pipelines or deployed ML models. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This will demonstrate your hands-on experience and problem-solving skills.
✨Understand MLOps Best Practices
Familiarise yourself with MLOps principles, including CI/CD, experiment tracking, and model governance. Be prepared to discuss how you’ve implemented these practices in past roles or how you would approach them in this new position.
✨Collaborate and Communicate
Since this role involves working closely with cross-functional teams, be ready to talk about your collaboration experiences. Highlight how you’ve effectively communicated complex technical concepts to non-technical stakeholders, ensuring everyone is on the same page.
