Assentian Limited
AI Engineer Intern
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Part-Time AI Engineer Intern
Location: Remote with regular meetups in London or Leeds
Type: Part-Time Internship
Duration: Initially 6 months
Compensation: Dependent on Skills and Experience
Internship Overview
We are looking for a Part-Time AI Engineer Intern to join our team and contribute to two cutting-edge research and development projects at the intersection of machine learning, telecommunications, and multi-sensor signal intelligence. This role is ideal for someone who wants hands-on experience building real AI systems that go beyond prototypes working with real data, real constraints, and real-world deployment considerations.
You will work closely with senior engineers and researchers across two parallel work streams: an LLM and graph neural network (GNN) based system for understanding and predicting live telecoms network behaviour, and a multi-modal signal classification system designed to remain robust under corrupted, incomplete, or adversarial conditions while continually learning to recognise new signal types.
This is a great opportunity for someone who wants to move beyond coursework and apply AI/ML techniques to genuinely hard, applied problems with direct exposure to how research-grade ideas get turned into working systems.
Key Responsibilities
You will contribute to work on:
- Designing and building data pipelines that transform raw network logs, telemetry, and sensor streams into structured representations suitable for model training and evaluation.
- Developing and experimenting with graph neural network architectures to model live telecoms network topology and behaviour over time.
- Exploring how large language models can be combined with GNNs to interpret network state, detect anomalies, and support natural-language reasoning over network conditions.
- Building and evaluating models for automatic fault detection and for improving energy and spectrum efficiency in mobile networks.
- Contributing to simulation environments used to generate training data and stress-test models against realistic network scenarios.
- Developing multi-modal classification models that fuse signals from multiple sensor types and remain reliable under corrupted, incomplete, or adversarially manipulated inputs.
- Implementing and testing techniques for continual/incremental learning, enabling models to recognise novel signal types without forgetting previously learned ones (addressing catastrophic forgetting).
- Running experiments, evaluating model performance, and clearly documenting methodology, results, and trade-offs.
- Collaborating with the wider engineering team on code reviews, technical discussions, and iterative model improvement.
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Skills and Qualifications
Preferred background: Currently pursuing, or recently completed, a Master's degree in AI, Machine Learning, Data Science, or a closely related field. High-calibre final-year Computer Science undergraduates with strong, demonstrable ML experience will also be considered.
Essential Skills:
- Strong programming ability in Python, with solid software engineering fundamentals (version control, testing, clean code practices).
- Practical experience with a deep learning framework such as PyTorch or TensorFlow.
- Solid understanding of core machine learning concepts: model training, evaluation, overfitting/regularisation, and experimentation methodology.
- Familiarity with neural network fundamentals, including sequence models and/or graph-based architectures.
- Comfort working with real-world, messy datasets — cleaning, transforming, and structuring data for model consumption.
- Strong analytical and problem-solving skills, with the ability to reason about model behaviour and failure modes.
- Good written and verbal communication skills, with the ability to document technical work clearly.
- Self-motivated and comfortable working part-time/flexibly within a small, fast-moving technical team.
Desirable Skills:
- Hands-on experience with graph neural networks (e.g. GCN, GAT, GraphSAGE) or graph-structured data.
- Exposure to large language models (LLMs), including fine-tuning, prompting, or retrieval-augmented approaches.
- Understanding of continual/incremental learning, few-shot/open-set recognition, or catastrophic forgetting mitigation techniques.
- Experience with multi-modal or multi-sensor data fusion.
- Familiarity with robustness/adversarial ML concepts (e.g. handling noisy, corrupted, or adversarially perturbed inputs).
- Background or coursework in telecommunications, network engineering, or signal processing.
- Experience with simulation frameworks or synthetic data generation for ML training.
- Familiarity with MLOps tooling (experiment tracking, containerisation, cloud compute environments).
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What You Will Gain
- Direct, hands-on experience building AI systems that combine cutting-edge techniques (LLMs, GNNs, continual learning, robust multi-modal classification) on genuinely novel applied problems.
- Exposure to the full lifecycle of applied AI development — from data pipeline design through modelling, simulation, and evaluation.
- Mentorship from senior engineers and researchers working at the intersection of AI, telecommunications, and signal intelligence.
- The opportunity to contribute to work with real-world impact on network reliability, energy efficiency, and spectrum management.
- A flexible, part-time structure that can accommodate ongoing academic commitments.
- A strong foundation and portfolio of applied AI/ML work to support further study or a full-time career in AI engineering.
- Potential for a reference, letter of recommendation, or future full-time opportunity based on performance.
To Apply
Please submit your CV to Dr Ilesh Dattani (ilesh.dattani@assentian.com) and a brief statement of interest outlining your relevant experience and what you hope to gain from this internship.
Job Types: Full-time, Part-time, Temporary, Fixed term contract, Freelance
Contract length: 6 months
Must be resident in the United Kingdom and eligible to work in the United Kingdom
Assentian Limited is a Cyber Security and AI Lab headquartered in the UK with offices in Ireland, Singapore and the United States of America. We are committed to equal opportunity for all applicants and employees. We do not discriminate on the basis of age, disability, gender reassignment, marriage or civil partnership, pregnancy or maternity, race, religion or belief, sex, or sexual orientation, in accordance with the Equality Act 2010. We welcome applications from all suitably qualified candidates regardless of background, and we are happy to discuss reasonable adjustments to the recruitment process for candidates who need them.
Benefits:
- Casual dress
- Work from home
- Flexitime
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