At a Glance
- Tasks: Lead the design and deployment of cutting-edge AI models for tactical hardware.
- Company: Join a forward-thinking tech company focused on defence and AI innovation.
- Benefits: Competitive pay, generous leave, and professional development opportunities.
- Other info: Inclusive culture with support for diverse backgrounds and career growth.
- Why this job: Make a real impact on national security with advanced AI technologies.
- Qualifications: 3+ years in ML model deployment and strong Python skills required.
The predicted salary is between 63000 - 77000 £ per year.
We are seeking a Senior Machine Learning Engineer to lead the design, quantization, and deployment of edge-native AI models and knowledge analytics engines. In this role, you will transition state-of-the-art Small Language Models (SLMs) and knowledge graph pipelines into air-gapped, degraded, and bandwidth-constrained tactical hardware. You will ensure all deployed AI capabilities comply with UK Defence standards for Dependable AI (JSP 936), delivering deterministic, explainable, and human-in-the-loop decision-support tools for intelligence and operational users.
Requirements
- Edge Model Optimization & Deployment: Quantize, fine-tune, and optimize open-source SLMs (e.g., Gemma 3, Llama 3) and vision-language models for execution on low-power edge runtimes (LiteRT / TensorFlow Lite, ONNX Runtime, ExecuTorch).
- Knowledge Analytics & Graph Processing: Design, implement, and maintain lightweight on-device graph databases and relationship extraction pipelines (Python, Rust, or C++) to process structured and unstructured sensor data.
- JSP 936 & AI Governance: Implement bounding guardrails, prompt evaluation, and anti-hallucination controls to ensure 100% compliance with MoD AI ethics, safety, and non-kinetic governance standards.
- Data & MLOps Pipelines: Build reproducible model training, evaluation, and containerised deployment pipelines capable of operating in air-gapped or low-bandwidth environments.
- Technical Client Advisory: Translate complex ML/AI concepts into clear technical recommendations for MoD stakeholders, DSTL assessors, and Prime contractors.
Required Qualifications & Experience
- Active UK SC Clearance (minimum).
- Strong understanding of AI safety, non-repudiation, and human-in-the-loop operational constraints (JSP 936 V1.1 / Dependable AI).
- 3+ years of production experience deploying ML models to edge runtime environments (LiteRT/TFLite, ONNX, C++ bindings).
- Experience in model quantization techniques (INT8, INT4, AWQ) and execution acceleration across NPU/GPU hardware.
- Proficiency in Python and PyTorch/HuggingFace ecosystems.
- Solid foundation in natural language processing (NLP), semantic summarisation, and graph-based data structures (Graph DBs, vector embeddings, network analysis).
- Understanding of data serialization formats (Protobuf, JSON, XML) and streaming analytics.
Desirable / Bonus Qualifications
- Experience integrating ML runtimes into Android ART (via Chaquopy, JNI, or native C++ libraries).
- Background in processing military sensor feeds, signals intelligence (SIGRF), or Cursor-on-Target (CoT) data.
- Publications or prior project delivery with DSTL, DAIC, or Defence Innovation programs.
Benefits
- Competitive Pay: Salaries reviewed annually to ensure they reflect your performance and market value.
- Loyalty Pension: We invest in your future. Starting at a 5% employer contribution, we increase this by 0.5% every year after your third anniversary, up to a maximum of 8%.
- Protection: Comprehensive Group Life Assurance for peace of mind.
Purpose & Culture
- Real Impact: Work on mission-critical projects that secure and improve the UK's digital infrastructure.
- Autonomy: A culture that empowers you to make decisions, prototype rapidly, and iterate towards success.
- Service & Community: We support those who serve. 10 paid days for Reservist Military Service.
Work / Life Balance
- Time Off: 25 days annual leave + Bank Holidays, with the flexibility to Buy/Sell additional days to suit your lifestyle.
- Giving back: 2 paid volunteering days per year.
Development & Growth
- Master Your Craft: Fully funded professional certifications (AWS, GCP, Agile, etc.) supported by 5 days paid study leave.
- Expand Your Horizons: An additional £500 annual "Personal Choice" fund to learn whatever inspires you—work-related or not.
- Support: Access to 1-2-1 professional coaching and team training to accelerate your career.
Health & Balance
- Premium Health: Vitality Private Medical Insurance (includes Apple Watch, gym discounts, and rewards).
- Flexibility: Genuine hybrid working with a WFH equipment allowance to perfect your home setup.
- Wellbeing: Cycle to Work scheme and a commitment to sustainable, healthy working practices.
Senior Machine Learning Engineer employer: Zaizi
Zaizi is an excellent employer for those looking to make a meaningful impact in government services through design. With a strong focus on professional development, competitive pay, and a supportive hybrid work culture, employees are encouraged to grow their skills while collaborating with a talented team. The opportunity to mentor fellow designers further enriches the experience, making Zaizi a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Zaizi or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Zaizi.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Zaizi.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Zaizi that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Zaizi.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Zaizi and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Zaizi
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Zaizi uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
