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 and opportunities for professional growth.
- Other info: Work in a dynamic environment with potential for career advancement.
- Why this job: Make a meaningful impact with your ML skills in a supportive team.
- Qualifications: Experience in building ML systems and proficiency in Python.
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 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, providing ample growth opportunities for engineers. 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 seeking meaningful and rewarding work.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - National Security AI Start-Up - Up to £98,000
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Few&Far. A personal connection can make all the difference when it comes to landing that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that demonstrate your ability to tackle messy data and real-world problems. This will help you stand out and show you mean business.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML tools. Practice coding challenges and be ready to discuss how you've deployed models in challenging environments. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Machine Learning Engineer - National Security AI Start-Up - Up to £98,000
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 experience with Python, ML systems, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to show us your personality and passion for applied ML. Share specific examples of how you've tackled real-world problems and how you enjoy working closely with end-users. Let us know why you're excited about this opportunity!
Showcase Your Projects:If you've built or contributed to any machine learning projects, make sure to include them in your application. We love seeing practical applications of your skills, especially if they involve messy data or production environments. It gives us a glimpse of what you can bring to the table!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team at Few&Far!
How to prepare for a job interview at Few&Far
✨Understand the Real-World Impact
Before your interview, take some time to research how the company’s machine learning systems are applied in high-stakes environments. Be ready to discuss specific examples of how you can contribute to solving real operational problems with your skills.
✨Showcase Your Prototyping Skills
Prepare to talk about your experience with rapid prototyping and turning ideas into production-ready systems. Bring examples of past projects where you’ve successfully navigated constraints like compute and latency, as this will demonstrate your practical mindset.
✨Communicate with Non-Technical Stakeholders
Since the role involves working closely with end-users, practice explaining complex technical concepts in simple terms. Think of scenarios where you’ve had to bridge the gap between technical and non-technical stakeholders, and be ready to share those experiences.
✨Demonstrate Your Python Proficiency
Brush up on your Python coding skills and be prepared to discuss your experience with modern ML tooling like Docker and CI/CD. You might even want to bring a code sample or two that showcases your clean, maintainable coding style, as this is crucial for collaboration.
