At a Glance
- Tasks: Transform real-world problems into reliable machine learning systems for national security.
- Company: Specialist AI start-up focused on high-stakes environments.
- Benefits: Competitive salary up to £98,000, career growth, and impactful work.
- Other info: Collaborative team environment with opportunities for ownership and innovation.
- Why this job: Make a difference with applied ML in meaningful projects that matter.
- Qualifications: Experience in building ML systems and strong Python skills.
The predicted salary is between 98000 - 98000 € per year.
Few&Far is working with a specialist AI company building machine learning systems used in genuinely high‐stakes environments. This is applied ML in the real world - messy data, limited infrastructure, and problems where outcomes actually matter.
They're looking for someone who enjoys taking ownership, working closely with end‐users, and building systems that hold up in production.
What you'll be doing
- Working directly with users to understand problems and shape solutions
- Rapidly prototyping ideas, then turning them into production‐ready systems
- Deploying models in environments with constraints (compute, latency, connectivity)
- Writing clean, maintainable Python code that others can build on
- Contributing to technical decisions and, over time, supporting more junior engineers
What they're looking for
- Confidence with Python and modern ML tooling (Docker, CI/CD, cloud, MLOps)
- A pragmatic mindset — able to balance speed, quality, and real‐world constraints
- Comfort working with non‐technical stakeholders and end‐users
- Eligibility for UK SC or DV clearance
Nice to have
- Experience in secure or government environments is useful, but not essential.
- Exposure to areas like NLP, computer vision, LLMs, APIs, or data pipelines will help — but the core requirement is strong applied ML experience.
Why this role?
It's a chance to work on problems that are genuinely meaningful, in a team that cares about building things properly — not just quickly. If you enjoy applied ML, ownership, and seeing your work used in the real world, this is a strong one to consider.
Machine Learning Engineer - National Security AI Start-Up - Up to £98,000 in London employer: Few&Far
Few&Far is an exceptional employer, offering a unique opportunity to work on impactful machine learning systems in the national security sector. With a strong emphasis on employee ownership and collaboration, the company fosters a culture of innovation and continuous learning, ensuring that team members can grow their skills while contributing to meaningful projects. Located in London or Gloucestershire, employees benefit from a supportive environment that values quality and real-world application, making it an ideal place for those passionate about applied ML.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - National Security AI Start-Up - Up to £98,000 in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨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, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common ML scenarios and problem-solving questions. Think about how you'd tackle real-world challenges, as this role is all about applying your knowledge to messy data and production systems.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in our mission. Tailor your application to highlight your experience with Python and ML tooling, and let us know why you're excited about this role.
We think you need these skills to ace Machine Learning Engineer - National Security AI Start-Up - Up to £98,000 in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your applied ML experience, especially in real-world scenarios, and don’t forget to mention any relevant projects or technologies you've worked with.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about applied machine learning and how you can contribute to our mission. Share specific examples of how you've tackled messy data or worked closely with end-users to shape solutions.
Showcase Your Technical Skills:Be sure to include your proficiency with Python and any modern ML tools like Docker or cloud services. If you've deployed models in challenging environments, let us know! We love seeing practical applications of your skills.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Few&Far
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and tools. Be ready to discuss your experience with Python, Docker, and any relevant ML frameworks. They’ll want to see that you can not only talk the talk but also walk the walk when it comes to building and deploying models.
✨Understand the User's Needs
Since this role involves working closely with end-users, take some time to think about how you would approach understanding their problems. Prepare examples of how you've previously collaborated with non-technical stakeholders to shape solutions. This will show that you can bridge the gap between tech and user needs.
✨Showcase Your Pragmatic Mindset
Be ready to discuss how you balance speed, quality, and real-world constraints in your projects. Think of specific instances where you had to make tough decisions or trade-offs. This will demonstrate that you’re not just a techie but someone who understands the bigger picture.
✨Prepare for Technical Questions
Expect some technical questions that might involve coding challenges or system design scenarios. Practice writing clean, maintainable code under pressure. You could even simulate a coding interview with a friend to get comfortable with articulating your thought process while solving problems.
