Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design, build, and deploy machine learning software and systems to solve real-world problems.
- Company: Join Faculty, a leader in impactful AI solutions for over 350 global customers.
- Benefits: Enjoy hybrid working, diverse teams, and the chance to learn from industry experts.
- Why this job: Be part of a dynamic team tackling high-impact challenges in government and public services.
- Qualifications: Experience with machine learning, Python, cloud architecture, and strong communication skills required.
- Other info: Roles are dynamic; expect to evolve alongside business needs and contribute to meaningful projects.
The predicted salary is between 42000 - 84000 ÂŁ per year.
Join to apply for the Machine Learning Engineer role at Faculty.
Why Faculty?
We established Faculty in 2014 because we believed AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human‑centric AI. We don’t chase hype cycles; we innovate, build, and deploy responsible AI that moves the needle. Our business is growing fast, and we’re always looking for individuals who share our intellectual curiosity and want to build a positive legacy through technology.
Our Life Sciences team focuses on building AI solutions to optimise the research and commercialisation of life‑changing therapies. We partner with major pharma firms, academic research centres, and MedTech start‑ups to design and deliver solutions that address critical healthcare challenges and help democratise health for all.
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 have 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.
Our Recruitment Ethos
We aim to grow the best team – not the most similar one. Diversity of individuals fosters diversity of thought and strengthens our pursuit of truth. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.
Some Of Our Standout Benefits
- Unlimited Annual Leave Policy
- Private healthcare and dental
- Enhanced parental leave
- Family‑Friendly Flexibility & Flexible working
- Sanctus Coaching
- Hybrid Working (2 days in our Old Street office, London)
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please do apply or reach out to our Talent Acquisition team for a confidential chat – talent@faculty.ai. We are open to conversations about part‑time roles or condensed hours.
Seniority level
- Entry level
Employment type
- Full‑time
Job function
- Engineering and Information Technology
Industries
- Technology, Information and Internet
#J-18808-Ljbffr
Machine Learning Engineer employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the specific machine learning frameworks mentioned in the job description, such as Scikit-learn, TensorFlow, and PyTorch. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your technical capabilities during discussions.
✨Tip Number 2
Engage with the AI community by attending meetups, webinars, or conferences focused on machine learning and AI applications. Networking with professionals in the field can provide valuable insights and potentially lead to referrals for the position.
✨Tip Number 3
Prepare to discuss real-world applications of machine learning that you've worked on or studied. Be ready to explain how you approached problems, the methodologies you used, and the impact of your solutions, as this aligns with Faculty's focus on high-impact systems.
✨Tip Number 4
Showcase your ability to work collaboratively by highlighting any past experiences where you’ve successfully partnered with cross-functional teams. This role requires working closely with data scientists and engineers, so demonstrating your teamwork skills will be crucial.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, software engineering, and any specific frameworks mentioned in the job description, such as Scikit-learn, TensorFlow, or PyTorch. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with Faculty's mission. Mention specific projects or experiences that demonstrate your ability to solve real-world problems using machine learning.
Showcase Your Technical Skills: Include a section in your application that details your technical expertise, particularly in cloud architecture, deployment, and containerisation with Docker and Kubernetes. Provide examples of how you've applied these skills in previous roles.
Demonstrate Communication Skills: Since outstanding verbal and written communication is crucial for this role, consider including a brief example of how you've effectively communicated complex technical concepts to non-technical stakeholders in your application.
How to prepare for a job interview at Faculty
✨Understand the Machine Learning Lifecycle
Make sure you can discuss the full machine learning lifecycle confidently. Be prepared to explain how you've worked with data scientists to deploy models into production and the challenges you've faced along the way.
✨Showcase Your Technical Skills
Highlight your experience with frameworks like Scikit-learn, TensorFlow, or PyTorch. Be ready to discuss specific projects where you used these tools, focusing on your role in building scalable ML systems.
✨Demonstrate Communication Skills
Since you'll be working with both technical and non-technical stakeholders, practice explaining complex concepts in simple terms. Prepare examples of how you've successfully communicated technical information in past roles.
✨Exhibit a Problem-Solving Mindset
Prepare to discuss how you've approached solving real-world problems using machine learning. Share specific examples that demonstrate your ability to think critically and find innovative solutions.
