At a Glance
- Tasks: Build and deploy impactful AI solutions for diverse clients using machine learning.
- Company: Join a forward-thinking company focused on real-world AI applications.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Fast-paced environment with autonomy to drive projects and solutions.
- Why this job: Make a real impact by bringing machine learning out of the lab and into practice.
- Qualifications: Experience with ML frameworks, strong Python skills, and cloud platform knowledge.
The predicted salary is between 50000 - 70000 £ per year.
About the Role
Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems.
What you'll be doing:
- Building and deploying production-grade ML software, tools, and infrastructure.
- Creating reusable, scalable solutions that accelerate the delivery of ML systems.
- Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.
- Leading technical scoping and architectural decisions to ensure project feasibility and impact.
- Defining and implementing Faculty’s standards for deploying machine learning at scale.
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.
Who we're looking for:
- You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- You possess strong Python skills and solid experience in software engineering best practices.
- You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
- You've worked with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale.
- You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.
- You thrive in a fast-paced environment and enjoy the autonomy to own scope, solve and deliver solutions.
Machine Learning Engineer in London employer: Faculty (faculty.ai)
As a Machine Learning Engineer at our company, you will be part of a dynamic and innovative team dedicated to delivering impactful AI solutions. We foster a collaborative work culture that encourages continuous learning and professional growth, providing you with the autonomy to lead projects and make significant contributions. Located in a vibrant area, we offer competitive benefits and a supportive environment that values your expertise and creativity.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the machine learning field, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those using frameworks like Scikit-learn, TensorFlow, or PyTorch. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on core ML concepts and software engineering best practices. Practice coding challenges and be ready to discuss your past projects in detail.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight how your experience aligns with our needs, and don’t forget to follow up after applying!
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with machine learning frameworks like Scikit-learn, TensorFlow, or PyTorch. We want to see how you've operationalised models and contributed to scalable software architecture.
Showcase Your Projects:Include specific examples of production-grade ML systems you've built or deployed. We love seeing real-world applications of your skills, so don’t hold back on the details!
Communicate Clearly:Since you'll be advising non-technical stakeholders, make sure your application reflects your ability to explain complex concepts simply. We appreciate clear communication, so let that shine through in your writing.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother process. It helps us keep track of your application and ensures you get the best chance to showcase your skills!
How to prepare for a job interview at Faculty (faculty.ai)
✨Know Your ML Frameworks
Make sure you brush up on your knowledge of frameworks like Scikit-learn, TensorFlow, and PyTorch. Be ready to discuss how you've operationalised models using these tools in past projects. This will show that you not only understand the theory but can also apply it practically.
✨Showcase Your Python Skills
Since strong Python skills are a must, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code. Familiarise yourself with software engineering best practices to impress your interviewers.
✨Understand Cloud Platforms
Get comfortable discussing cloud platforms like AWS, Azure, or GCP. Be prepared to explain how you've used these platforms for deploying ML solutions, including any architectural decisions you've made. This will highlight your hands-on experience and technical expertise.
✨Communicate Clearly
As an excellent communicator, you’ll need to translate complex ML concepts for non-technical stakeholders. Practice explaining your past projects in simple terms, focusing on the impact and value they brought to clients. This will demonstrate your ability to bridge the gap between technical and non-technical teams.
