At a Glance
- Tasks: Lead innovative AI projects and set technical direction for complex machine learning solutions.
- Company: Join Faculty, a pioneering AI company transforming industries with human-centric technology.
- Benefits: Enjoy unlimited annual leave, private healthcare, and flexible working options.
- Other info: Diverse and inclusive culture that values intellectual curiosity and positive impact.
- Why this job: Shape the future of AI while making a real-world impact in defence and beyond.
- Qualifications: Expertise in Python, cloud solutions, and experience leading engineering teams required.
The predicted salary is between 70000 - 90000 £ per year.
We established Faculty in 2014 because we thought that 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 which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty. Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work between 2 to 4 days per week on-site with these customers which may require travel to locations throughout the UK. When not required on client sites, you’ll have the flexibility to work from our London office or remotely from elsewhere within the UK.
As a Lead Machine Learning Engineer, you will spearhead the technical direction and delivery of complex, innovative AI projects. You will act as a technical expert, applying your skills across various projects from AI strategy to client-side deployments, while ensuring architectural decisions are sound and reliable. This role demands a balance of deep technical expertise and strong leadership, focusing on driving innovation, fostering team growth, and building reusable solutions across the organisation.
What you'll be doing:
- Setting the technical direction for complex ML projects, balancing trade-offs, and guiding team priorities.
- Designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions.
- Defining project problems, developing roadmaps, and overseeing delivery across multiple workstreams in often ill-defined, high-risk environments.
- Driving the development of shared resources and libraries across the organisation and guiding other engineers in contributing to them.
- Leading hiring processes, making informed selection decisions, and mentoring multiple individuals to foster team growth.
- Proactively developing and executing recommendations for adopting new technologies and changing our ways of working to stay ahead of the competition.
- Acting as a technical expert and coach for customers, accurately estimating large work-streams and defending rationale to stakeholders.
Who we're looking for:
- You are a technical expert among your peers, capable of going deep on particular topics and demonstrating breadth of knowledge to solve almost any problem.
- You possess strong Python skills and practical experience operationalising models using frameworks like Scikit-learn, TensorFlow, or PyTorch.
- You are an expert in at least one major Cloud Solution Provider (e.g., Azure, GCP, AWS) and have led teams to build full-stack web applications.
- You have hands-on experience with containerisation tools like Docker and orchestration via Kubernetes.
- You can successfully manage and coach a team of engineers, setting team-wide development goals to improve client delivery.
- You find novel, clever solutions for project delivery and take ownership for successful project outcomes.
- You're an excellent communicator who can proactively help customers achieve their goals and guide both technical teams and non-technical stakeholders.
The Interview Process:
- Talent Team Screen (30 minutes)
- Introduction to the role (45 minutes)
- Pair Programming Interview (90 minutes)
- System Design Interview (90 minutes)
- Commercial & Leadership Interview (60 minutes)
Our Recruitment Ethos:
We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. 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
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.
Remote Lead Machine Learning Engineer in Edinburgh employer: Faculty AI
At Faculty, we pride ourselves on being an exceptional employer that champions innovation and ethical AI solutions. Our collaborative work culture fosters intellectual curiosity and offers unparalleled opportunities for professional growth, with benefits like unlimited annual leave, private healthcare, and family-friendly flexibility. Join us in London or work remotely within the UK, and be part of a team that is dedicated to making a meaningful impact in the defence sector and beyond.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Lead Machine Learning Engineer in Edinburgh
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Faculty AI!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Remote Lead Machine Learning Engineer at Faculty AI.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Faculty AI.
✨Apply Directly through Our Website
When you find a suitable opening like Remote Lead Machine Learning Engineer at Faculty AI, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Remote Lead Machine Learning Engineer in Edinburgh
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Faculty AI, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Faculty AI. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Faculty AI
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Faculty AI!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
