At a Glance
- Tasks: Lead the development of cutting-edge AI systems and mentor junior engineers.
- Company: Join Faculty, a leader in responsible AI innovation since 2014.
- Benefits: Competitive salary, diverse team, and opportunities for impactful work.
- Other info: Dynamic environment with a focus on diversity and intellectual curiosity.
- Why this job: Make a real difference in AI for national security and global stability.
- Qualifications: Experience with ML frameworks, strong Python skills, and cloud platforms.
The predicted salary is between 63000 - 77000 £ 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 business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.
Our National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability. From strengthening mission-critical capabilities across national security and intelligence, to working with frontier labs to provide robust AI safety red teaming and evaluation, we work at the frontier of high-stakes, high-impact missions. We understand that powerful AI systems bring both transformative opportunities and complex risks and we are proud to partner with Government and the biggest tech organisations in the world to ensure AI is not just transformative but is also secure, trustworthy and safe for all. Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Developed Vetting (DV) and willing to work on site with our clients from time to time.
As a Senior Machine Learning Engineer, we'll look to you to lead development and deployment of cutting-edge AI systems for our diverse clients. You'll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills.
What You'll Be Doing
- Leading technical scoping and architectural decisions for high-impact ML systems
- Designing and building production-grade ML software, tools, and scalable infrastructure
- Defining and implementing best practices and standards for deploying machine learning at scale across the business
- Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
- Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
- Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth
Who We're Looking For
- You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch
- You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems
- You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure
- You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
- You thrive in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion
- You communicate exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders
Our Interview Process
- Talent Team Screen (30 minutes)
- Pair Programming Interview (90 minutes)
- System Design Interview (90 minutes)
- Commercial 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.
Senior Forward Deployed Engineer employer: Faculty
At Faculty, we pride ourselves on being an exceptional employer, particularly for the Head of Banking role, where you will lead transformative AI initiatives in a dynamic banking landscape. Our culture fosters innovation and collaboration, offering unlimited annual leave, private healthcare, and family-friendly flexibility to ensure a healthy work-life balance. With a strong commitment to employee growth through mentorship and coaching, we empower our team to thrive in a supportive environment that values diverse perspectives and encourages meaningful contributions.
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We think this is how you could land Senior Forward Deployed Engineer
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Senior Forward Deployed Engineer
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, 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. 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
✨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!
✨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.
