At a Glance
- Tasks: Design, build, and deploy machine learning software and infrastructure for impactful projects.
- Company: Join Faculty, a leader in human-centric AI with 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 solving real-world problems with cutting-edge AI technology.
- Qualifications: Experience in machine learning lifecycle, Python, and cloud architecture is preferred.
- Other info: Opportunity for Security Clearance and mentorship roles available.
About Faculty
At Faculty, we transform organisational performance through safe, impactful and human-centric AI.
With more than a decade of experience, we provide over 350 global customers with software, bespoke AI consultancy, and Fellows from our award winning Fellowship programme.
Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI.
Should you join us, youll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.
We operate a hybrid way of working, meaning that you\’ll split your time across client location, Faculty\’s Old Street office and working from home depending on the needs of the project.
About the Role
You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in the Government & Public Services arena.
Because of the potential to work with our clients in the National Security space, you will need to be eligible for Security Clearance, details of which are outlined when you click through to apply.
What You\’ll Be Doing
You are engineering-focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting-edge ML applications into the real world. You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements. You will support both technical, and non-technical stakeholders, to deploy ML to solve real-world problems.
Our Machine Learning Engineerings are responsible for the engineering aspects of our customer delivery projects. As a Machine Learning Engineer, youll be essential to helping us achieve that goal by:
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Building software and infrastructure that leverages Machine Learning;
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Creating reusable, scalable tools to enable better delivery of ML systems
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Working with our customers to help understand their needs
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Working with data scientists and engineers to develop best practices and new technologies; and
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Implementing and developing Facultys view on what it means to operationalise ML software.
As a rapidly growing organisation, roles are dynamic and subject to change. Your role will evolve alongside business needs, but you can expect your key responsibilities to include:
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Working in cross-functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high-impact systems.
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Working with senior engineers to scope projects and design systems
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Providing technical expertise to our customers
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Technical Delivery
Who We\’re Looking For
You can view our company principles here. We look for individuals who share these principles and our excitement to help our customers reap the rewards of AI responsibly.
To succeed in this role, youll need the following – these are illustrative requirements and we dont expect all applicants to have experience in everything (70% is a rough guide):
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Understanding of, and experience with the full machine learning lifecycle
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Working with Data Scientists to deploy trained machine learning models into production environments
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Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch
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Experience with software engineering best practices and developing applications in Python.
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Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GPS or Azure)
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Demonstrable experience with containers and specifically Docker and Kubernetes
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An understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques
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Demonstrable experience of managing/mentoring more junior members of the team
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Outstanding verbal and written communication.
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Excitement about working in a dynamic role with the autonomy and freedom you need to take ownership of problems and see them through to execution
We like people who combine expertise and ambition with optimism — who are interested in changing the world for the better — and have the drive and intelligence to make it happen. If youre the right candidate for us, you probably:
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Think scientifically, even if youre not a scientist – you test assumptions, seek evidence and are always looking for opportunities to improve the way we do things.
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Love finding new ways to solve old problems – when it comes to your work and professional development, you dont believe in good enough. You always seek new ways to solve old challenges.
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Are pragmatic and outcome-focused – you know how to balance the big picture with the little details and know a great idea is useless if it cant be executed in the real world.
What we can offer you:
The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.
Faculty is the professional challenge of a lifetime. Youll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.
Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and youll learn something new from everyone you meet.
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Machine Learning Engineer (London) employer: The Rundown AI, Inc.
Contact Detail:
The Rundown AI, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (London)
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and AI. Follow industry leaders on platforms like LinkedIn or Twitter, and engage with their content. This will not only enhance your knowledge but also show your passion for the field during interviews.
✨Tip Number 2
Network with professionals in the AI and machine learning community. Attend meetups, webinars, or conferences where you can connect with people from Faculty or similar organisations. Building these relationships can lead to valuable referrals and insights about the role.
✨Tip Number 3
Prepare to discuss real-world applications of machine learning that you've worked on. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This practical experience will resonate well with the interviewers at Faculty.
✨Tip Number 4
Understand the importance of security clearance for this role. Research what it entails and be prepared to discuss your eligibility and any relevant experiences that demonstrate your reliability and integrity in handling sensitive information.
We think you need these skills to ace Machine Learning Engineer (London)
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 enthusiasm for the role and the company. Discuss how your skills align with their mission of transforming organisational performance through AI. Mention specific projects or experiences that demonstrate your ability to work with machine learning in real-world applications.
Showcase Your Technical Skills: Include a section in your CV or cover letter that details your technical skills, particularly in cloud architecture, deployment, and containerisation (Docker and Kubernetes). Provide examples of how you've applied these skills in previous roles or projects.
Highlight Communication Skills: Since the role involves working with both technical and non-technical stakeholders, emphasise your verbal and written communication skills. Provide examples of how you've successfully communicated complex technical concepts to diverse audiences in the past.