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- Tasks: Design and deploy machine learning models and optimise data pipelines for real-world applications.
- Company: RealityMine, a leader in data-driven insights for top global brands.
- Benefits: Hybrid working model, competitive salary, and opportunities for professional growth.
- Why this job: Join a pioneering team and make a real impact with cutting-edge machine learning technology.
- Qualifications: Experience with machine learning frameworks like PyTorch and strong problem-solving skills.
- Other info: Collaborative environment with a focus on innovation and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
RealityMine has been a pioneer in delivering data driven insights to the world's largest brands for over a decade. Our platform provides unique data solutions to our clients enabling them to make strategic, informed decisions powered by data from real people, collected in a privacy safe way. We are now looking for a Machine Learning Engineer to help build and optimise production-ready machine learning systems and advance our data pipelines for inference at scale. This is a pivotal role for someone who thrives in autonomous environments and enjoys owning solutions from concept to deployment.
The Role: As a Machine Learning Engineer, you will design, develop, and deploy models and pipelines that power RealityMine’s RealLife App and Web products. You will work with stakeholders across technical and non-technical teams to scope requirements, optimise performance, and ensure our systems scale to handle Big Data. This is a hands-on role that requires strong engineering practices, creative problem solving, and analytical thinking. You will take full ownership of projects, including discovering and defining problems, cleaning and preparing data in the warehouse, developing and testing models, and deploying solutions into production environments.
Our offices are in Trafford Park, Manchester and the role consists of hybrid working, where we ask for our team to be in the office for collaboration and team building 2 days per week. The rest of the week is up to you; deep focus at home, or more of the same!
Key Responsibilities:
- Build, maintain, and productionise machine learning models using PyTorch or similar frameworks.
- Design and optimise data pipelines for ML inference at scale.
- Work with large datasets using Big Data tools.
- Deploy ML workflows using AWS infrastructure.
- Collaborate with data engineers and product teams to integrate ML solutions into RealityMine’s products.
- Investigate and troubleshoot customer feedback on data quality and drive model improvements from experimentation.
- Promote best practices in ML engineering and data quality across the business.
- Adhere to Company Policies and Procedures with respect to Security, Quality and Health.
Machine Learning Engineer employer: RealityMine
Contact Detail:
RealityMine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to current or former employees at RealityMine on LinkedIn. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, let us see what you've built and how you tackle real-world problems.
✨Tip Number 3
Prepare for the technical interview! Brush up on your PyTorch skills and be ready to discuss your approach to building and optimising data pipelines. We love seeing candidates who can think on their feet!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the RealityMine team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Your Passion for Machine Learning: When writing your application, let us see your enthusiasm for machine learning! Share any personal projects or experiences that highlight your skills and creativity in this field. We love candidates who are genuinely excited about what they do.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for the Machine Learning Engineer role. Highlight relevant experience with PyTorch, data pipelines, and AWS. This shows us you’ve done your homework and understand what we’re looking for!
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your qualifications and experiences. We appreciate a well-structured application that gets straight to the good stuff!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about RealityMine and what we stand for.
How to prepare for a job interview at RealityMine
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially PyTorch and AWS. Brush up on your knowledge of machine learning models and data pipelines, as you’ll likely be asked to discuss your experience with these tools during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex problems in previous roles. Think about times when you had to clean and prepare data or optimise a model for performance. This will demonstrate your analytical thinking and creative problem-solving abilities.
✨Understand the Company’s Mission
Familiarise yourself with RealityMine’s focus on data-driven insights and privacy. Be ready to discuss how your skills can contribute to their mission and how you can help enhance their RealLife App and Web products. Showing genuine interest in their work will set you apart.
✨Prepare for Collaboration Questions
Since the role involves working with both technical and non-technical teams, think about your past experiences collaborating across different departments. Be prepared to discuss how you’ve communicated complex ideas to non-technical stakeholders and how you’ve integrated feedback into your projects.
