đź“ŤLocation:[Manchester - Hybrid / Home working]
đź’ĽJob Type:Full-time
đź’·Salary:ÂŁ50,000 - ÂŁ80,000 base
AI / ML Engineer – Start-Up – Stock Consideration
We are looking for a mid-level AI / Machine Learning Engineer to join a vibrant early tech start-up in Manchester.
Do you love turning machine learning models into intelligent, real-world applications? This is your chance to join a cutting-edge start-up team shaping the brainpower behind Behavioural biometrics and AI.
Our client leverages behavioural biometric interactions and powerful AI to create unique user profiles for seamless security.
We are looking for candidates with expertise insensor-based data,large behaviour data, orbehavioural biometrics.Ideal applicants will have experience analysing and interpreting complex behavioural data to drive insights and innovation. You will be a skilled professional with a strong history of turning prototypes into robust, production-ready solutions that drive meaningful impact.
This is your chance to join the early stages of growth and play a key role in shaping the future before they scale globally. Don't miss the opportunity to be part of the original team driving this innovation forward! Why not join during the seed growth and funding stage for a chance at early equity consideration
Due to recent investments and ambitious growth plans, they are looking for an AI Engineer to join the team
The Role:As an AI / ML Engineer, you’ll transform complex behavioural data into responsive, intelligent, and scalable systems that think and adapt in real time.
- Architect and deploy machine learning models from idea to production.
- Build robust APIs and microservices to serve AI models at scale.
- Integrate behavioural intelligence models across cloud platforms (AWS, GCP, Azure).
- Set up end-to-end MLOps pipelines: monitoring, retraining, and automation.
- Collaborate with cross-functional teams to align tech with user-centric product design.
What We’re Looking For:
- 2+ years in AI/ML engineering or backend software roles with ML components.
- Proficiency in Python and frameworks like PyTorch/TensorFlow, Scikit-learn.
- Experience deploying models with Docker, Kubernetes, or serverless architectures.
- Solid grasp of MLOps workflows, versioning, and cloud automation.
- Strong foundations in algorithms, data structures, and system design.
- Bonus: Familiarity with behavioural biometrics, sensor-based or time series data
- An entrepreneurial mindset—curious, autonomous, and passionate about human-centred AI.
