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For Companies At a Glance
- Tasks: Build intelligent analytics systems using AI, data engineering, and automation.
- Company: Dynamic media company at the forefront of AI and data technology.
- Benefits: Competitive salary, equity participation, mentorship, and annual learning budget.
- Why this job: Make a real impact on high-profile projects while developing your skills in AI.
- Qualifications: Experience in data analytics, strong SQL and Python skills, and a passion for AI.
- Other info: Hybrid work model with clear career progression and hands-on exposure to advanced tools.
The predicted salary is between 48000 - 84000 £ per year.
Location: London (Hybrid 3 days in office)
Industry: Media, Campaign Media, AI/Data
Salary: 60-70k
About the Role
We are hiring an AI Data Engineer to help build the next generation of intelligent analytics systems. The role combines data engineering, automation, and applied AI giving you the chance to shape how complex datasets are processed, analysed, and turned into insights through large language models (LLMs) and automated pipelines. This is an excellent opportunity for someone early in their career (12+ years experience) who is ready to step into a role with impact. You will work closely with experienced engineers and gain hands-on exposure to advanced tools, scalable data systems, and AI-powered reporting automation.
What you will do
- Contribute to the design and maintenance of analytics pipelines, ensuring reliability and performance.
- Use SQL and Python to build data workflows, automation scripts, and reporting processes.
- Support the integration of AI and LLMs into reporting and query-generation systems.
- Develop dashboards and automated insights for business stakeholders.
- Collaborate across technical and non-technical teams to translate data into clear recommendations.
- Learn how to evolve manual workflows into scalable, automated intelligence systems.
What we are looking for
- Experience in data analytics, data engineering, or campaign analytics.
- Strong SQL skills, plus Python (or R) for data processing and automation.
- Interest in AI/LLM applications; hands-on experience welcome but not essential.
- Understanding of digital performance metrics and data connectors (experience with platforms like Google Ads, Meta, or DSPs a bonus).
- Familiarity with large datasets (e.g. BigQuery, Snowflake, or other cloud platforms).
- Strong communication skills and a problem-solving mindset.
What is on offer
- Salary in the region of 60-70K plus equity participation.
- Direct mentorship from senior engineers on advanced AI and automation projects.
- Opportunity to work on high-impact data systems used by major global clients.
- Clear career progression as the data team expands.
- Hybrid working with 3 days a week in our central London office.
- Annual learning budget for technical training, conferences, and AI/ML development.
Machine Learning Engineer employer: Immersum
Contact Detail:
Immersum Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the field. Building relationships can lead to job opportunities that aren’t even advertised.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving Python, SQL, or AI applications. Share it on platforms like GitHub or your personal website. This gives potential employers a taste of what you can do before they even meet you!
✨Ace the Interview
Prepare for technical interviews by practicing coding challenges and understanding data engineering concepts. Don’t forget to brush up on your communication skills too; being able to explain your thought process is key!
✨Apply Through Our Website
When you find a role that excites you, apply directly through our website. It shows your enthusiasm and makes it easier for us to track your application. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your data engineering and Python experience, as well as any relevant projects you've worked on. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about the role and how your background aligns with our needs. Don’t forget to mention your interest in AI and LLM applications – we love that!
Showcase Your Projects: If you've worked on any relevant projects, whether in a professional or personal capacity, make sure to include them. We’re keen to see how you’ve applied your skills in real-world scenarios, especially with data pipelines and automation.
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 don’t miss out on any important updates. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Immersum
✨Know Your Tech Stack
Make sure you’re well-versed in Python and SQL, as these are crucial for the role. Brush up on your data engineering skills and be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data challenges. Think about specific instances where you’ve turned data into actionable insights or automated workflows, as this will resonate with the interviewers.
✨Understand AI and LLM Applications
Even if you don’t have hands-on experience, show your enthusiasm for AI and large language models. Familiarise yourself with their applications in analytics and be ready to discuss how they can enhance reporting processes.
✨Communicate Clearly
Since collaboration is key in this role, practice explaining technical concepts in simple terms. Be prepared to demonstrate how you would translate complex data findings into clear recommendations for non-technical stakeholders.
