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 and excellent career advancement opportunities.
- Why this job: Make a real difference 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 employer: Faculty (faculty.ai)
Join a forward-thinking company that values innovation and collaboration, where as a Machine Learning Engineer, you will have the opportunity to work on impactful AI solutions in a dynamic environment. With a strong emphasis on employee growth, we offer continuous learning opportunities and a culture that encourages creativity and technical excellence. Located in a vibrant area, our workplace fosters teamwork and provides access to cutting-edge resources, making it an ideal setting for those looking to make a meaningful impact in the field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the machine learning field, attend meetups, and join 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. Whether it's a GitHub repo or a personal website, let potential employers see what you can do with frameworks like TensorFlow and PyTorch.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex ML concepts in simple terms, as you'll need to communicate effectively with both technical teams and non-technical stakeholders.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight your experience with cloud platforms and software engineering best practices.
We think you need these skills to ace Machine Learning 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 frameworks like Scikit-learn, TensorFlow, or PyTorch, and don’t forget to showcase your Python skills and cloud platform experience!
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 your skills can help us deliver impactful AI solutions for our clients. Keep it engaging and relevant!
Showcase Your Projects:If you've worked on any cool ML projects, make sure to mention them! Whether it's deploying production-grade software or using Docker and Kubernetes, we want to see how you've applied your skills in real-world scenarios.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and get you one step closer to joining our team!
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 in the past and share specific examples of projects where you’ve used these tools.
✨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 that adheres to software engineering best practices.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP. Be prepared to discuss your experience with architecture and security in these environments, as well as how you’ve deployed ML solutions at scale.
✨Communicate Clearly
As an excellent communicator, you’ll need to explain complex ML concepts to non-technical stakeholders. Practice simplifying your explanations and think of ways to relate technical details to real-world applications that clients can understand.
