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- Tasks: Develop and optimise machine learning models for real-world applications.
- Company: Fast-growing AI scale-up focused on innovative systems.
- Benefits: Competitive salary, flexible work options, and growth opportunities.
- Why this job: Join a team building scalable AI systems that make a real impact.
- Qualifications: Experience in machine learning and a passion for engineering.
- Other info: Collaborative environment with exciting challenges and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
🤖 Machine Learning Engineer | Scalable AI Systems
A fast-growing AI scale up is building the next generation of intelligent, high-performance systems – combining machine learning and systems optimization to power real-world applications at scale.
As an ML Engineer, you’ll take models from idea to production – implementing, optimizing, and scaling them to handle massive workloads efficiently. You will be collaborating closely with other engineers to build AI systems that are reliable, maintainable, and ready for real-world deployment.
💡 What you’ll do:
• Develop, deploy, and optimize ML models for production use
• Integrate & monitor AI pipelines with engineering teams
• Ensure models are efficient, scalable, & aligned with product goals
If you’re excited to engineer AI systems that truly scale, this is your chance to make a meaningful impact.
Machine Learning Engineer employer: Acceler8 Talent
Contact Detail:
Acceler8 Talent 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 folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects. We recommend including real-world applications and optimisations you've worked on. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss how you’ve taken models from idea to production. We suggest practicing common ML scenarios and problem-solving questions to impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! We make it easy for you to find roles that match your skills. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for machine learning and AI shine through. We want to see that you’re not just looking for a job, but that you’re genuinely excited about building scalable AI systems and making a real impact.
Highlight Relevant Experience: Make sure to showcase any experience you have with developing, deploying, or optimising ML models. We love seeing specific examples of how you've tackled challenges in the past, especially if they relate to real-world applications.
Tailor Your Application: Don’t just send out the same application everywhere! Take the time to tailor your CV and cover letter to our job description. Mention how your skills align with what we’re looking for, especially around collaboration and system optimisation.
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 you’re serious about joining our team!
How to prepare for a job interview at Acceler8 Talent
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've worked with in detail. Be prepared to explain how you developed, deployed, and optimised them, as well as the challenges you faced and how you overcame them.
✨Understand the Company’s AI Vision
Research the company’s current projects and their approach to AI systems. This will help you align your answers with their goals and demonstrate that you're genuinely interested in contributing to their mission.
✨Showcase Your Collaboration Skills
Since you'll be working closely with other engineers, be ready to share examples of successful collaborations. Highlight how you’ve integrated and monitored AI pipelines in past roles, and how teamwork led to better outcomes.
✨Prepare for Technical Questions
Brush up on your technical knowledge related to scalable AI systems. Expect questions about algorithms, data handling, and system optimisation. Practising coding problems or case studies can also give you an edge.