Machine Learning Engineer, AI (SFIA 3)
Zaizi
| Company | Zaizi |
| Category | Engineering |
| Location | Cheltenham |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 20 Jul 2026 |
| Last verified | 7 Aug 2026 |
| Source | Employer ATS (workable) |
Description
Work on exciting public sector projects and make a positive difference in people’s lives. At Zaizi, we thrive on solving complex challenges through creative thinking and the latest tools and tech. As a Machine Learning Engineer, you’ll research machine learning models and evaluate how they can be applied within our specialist domain, working with a small team who deliver AI capabilities for the UK government. You'll be curious about all flavours of AI from classical ML to the latest generative and adversarial techniques and keen to keep learning as the field moves. You'll bring a general understanding of software engineering alongside your ML knowledge and enjoy research, experimenting with new approaches and figuring out how they translate into practical capability for our customers. You'll work closely with a small, high-performing team of engineers and researchers, contributing ideas and picking up new skills as you go. Our work culture is inclusive, modern, friendly, and democratic. We look for bright, positive thinking individuals with a can do attitude and a genuine appetite for learning. Our people enjoy challenging themselves to be the best at what they do, if that sounds like you, you'll fit right in! And the work itself matters: you'll be helping to keep the UK safe. Requirements These are the expected objectives for this role. We are happy to discuss this further during the interview process with the successful candidate. Model Research & Evaluation: Research emerging machine learning models and techniques, and assess how they could be applied within our specialist domain. Applied AI Research: Turn research into practical proofs of concept, exploring generative, adversarial and other AI approaches. Domain Application: Work with the team to understand our customers problems in depth and translate ML capability into solutions that fit their specialist domain. Continuous Learning: Keep pace with the fast moving field of AI building knowledge across different model types and techniques and sharing what you learn with the team. Core Competencies Research Skills: Comfortable reading and evaluating ML research and translating findings into practical ideas worth testing. Technology Implementation: Ability to evaluate new AI technologies generative, adversarial or otherwise for relevance and feasibility within the UK Government Domain. Advanced Prototyping: Ability to build proof of concept applications that bridge the gap between initial research and real-world application. Specialist Advice: Keen to grow into a technical resource for the practical application of AI within the organisation, sharing knowledge as your expertise builds. Data Science: Applying data science techniques to support model research, evaluation, and refinement. Team Fit: A quick learner who's easy to work with, and will slot naturally into a close-knit, high-performing team. Required Skills Technical Expertise: Solid understanding of machine learning algorithms and frameworks with a general grounding in software engineering practices and a genuine curiosity about how ML models work. Python: Strong proficiency and hands-on programming experience in Python are required. ML Frameworks: Practical experience working with machine learning frameworks (e.g. Pytorch) to build, train, evaluate, and deploy models. Version Control: Proficiency with Git for version control and collaborative development is required. Containerisation: Familiarity with Docker is required to package, deploy, and run applications across environments. Additional / Welcome Skills (nice to have) Frontend Development: Experience with frontend technologies, specifically JavaScript and React, to help build interactive user interfaces for our prototypes. Cloud Services: Experience working with cloud platforms (e.g., AWS, Azure, GCP). Systems/Backend Languages: Familiarity with other languages used for high-performance backen