About this role
Salary: £61,000 - 101,000 per year
Requirements: 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.Right to work in the UK without sponsorship.Must have lived in the UK continuously for the last 5 years.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. Responsibilities: Lead the design, quantization, and deployment of edge-native AI models and knowledge analytics engines.Transition state-of-the-art Small Language Models (SLMs) and knowledge graph pipelines into air-gapped, degraded, and bandwidth-constrained tactical hardware.Quantize, fine-tune, and optimize open-source SLMs and vision-language models for execution on low-power edge runtimes.Design, implement, and maintain lightweight on-device graph databases and relationship extraction pipelines to process structured and unstructured sensor data.Implement bounding guardrails, prompt evaluation, and anti-hallucination controls to ensure compliance with MoD AI ethics, safety, and non-kinetic governance standards.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. Technologies: AIAndroidCursorHardwareJSONJSPMachine LearningModel TrainingNetworkPyTorchPythonXMLAWSCloudGCPSupportLESSMLOpsRustSecurityTensorFlow More:
hackajob is partnering directly with Zaizi to hire a Senior Machine Learning Engineer. We work on mission-critical projects that secure and improve the UKs digital infrastructure, and we offer a culture with real autonomy, rapid prototyping, and iteration. Our benefits include competitive pay reviewed annually, a loyalty pension with employer contributions starting at 5%, comprehensive group life assurance, 25 days annual leave plus Bank Holidays, the option to buy or sell additional days, two paid volunteering days per year, fully funded professional certifications with paid study leave, a £500 annual Personal Choice fund, 1-2-1 coaching and team training, Vitality Private Medical Insurance, hybrid working with a WFH equipment allowance, a Cycle to Work scheme, and 10 paid days for Reservist Military Service. We actively welcome applications from people of colour, the LGBTQ community, individuals with disabilities, neurodivergent individuals, parents, carers, and those from lower socio-economic backgrounds, and we can make adjustments throughout the interview process.
last updated 37 week of 2026