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 salary, comprehensive pension, and 25 days annual leave.
- Other info: Inclusive workplace welcoming diverse applicants and offering 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. 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).
- 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: Comprehensive Group Life Assurance for peace of mind.
- Real Impact: Work on mission-critical projects that secure and improve the UK's digital infrastructure.
- Service & Community: We support those who serve.
- Time Off: 25 days annual leave.
We're Hiring: Machine Learning Engineer, Senior in Bracknell employer: Hackajob Ltd
JPMorgan Chase is an exceptional employer, offering a dynamic work environment where innovation and collaboration thrive. As a Lead Site Reliability Engineer, you will not only tackle complex challenges but also benefit from extensive professional development opportunities and a strong commitment to diversity and inclusion. Located in a global financial hub, you'll be part of a team that values your expertise and encourages a culture of continuous improvement and technical excellence.
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We think this is how you could land We're Hiring: Machine Learning Engineer, Senior in Bracknell
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We think you need these skills to ace We're Hiring: Machine Learning Engineer, Senior in Bracknell
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 Hackajob Ltd.
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How to prepare for a job interview at Hackajob Ltd
✨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 Hackajob Ltd 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.
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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.
