Junior Machine Learning Engineer

Key Responsibilities

• Design, train and optimise predictive models using advanced architectures such as gradient boosted trees, temporal models and embedding based models. 
• Build robust training, evaluation and monitoring pipelines to ensure model quality, reproducibility and auditability. 
• Implement feature engineering, hyperparameter tuning, model debugging and performance optimisation. 
• Productionise models so they run reliably and efficiently at scale in client environments.

• Own schema aware data flows for modelling and cohorts; validate, transform and version datasets used in training and inference. 
• Manage and evolve database schemas; optimise SQL, indexing and partitioning for large training and scoring workloads. 

• Lead the modelling and data engineering components of client projects alongside DACs and Business Consultants. 
• Acquire and extract data from client source systems 
• Build and validate cohort logic to ensure accuracy, interpretability and alignment with client needs. 
• Troubleshoot and resolve complex modelling and pipeline issues throughout delivery.

• Build and integrate LLM based components including embedding pipelines, RAG workflows and text analysis models. 
• Develop and deploy agentic and multicomponent AI systems using modern ML frameworks. 
• Engineer high performance NLP and sequence models for information extraction, classification and risk prediction. 

• Configure advanced OneView components linked to modelling outputs such as risk logic, summaries and scoring pathways. 
• Contribute modelling innovations, performance insights and engineering improvements back into the platform. 

• Act as an SME for machine learning, AI and model engineering within DACs. 
• Mentor DACs on Python, modelling best practice, data engineering fundamentals and debugging approaches. 
• Produce documentation, templates and reusable components to raise engineering standards across delivery. 

What are we looking for?

• Strong Python engineering skills and experience with modern ML frameworks 
• Practical experience training and evaluating models (tree based, temporal, embedding/NLP or LLM based) 
• Ability to build reproducible training and evaluation pipelines 
• Experience containerising and deploying models (e.g., Docker, Fast API) 

• Strong SQL and experience working with relational databases 
• Understanding of schemas, data transformations and (ideally) DBT
• Experience preparing data for model training and scoring

• Working with embeddings, vector databases or RAG style workflows 
• Experience applying NLP or sequence models to real world datasets 

• Comfortable defining data requirements, discussing modelling decisions and troubleshooting issues in real time 

• Able to explain technical concepts simply and work closely with data scientists, engineers and consultants 

• Experience with Azure ML, AKS or similar cloud environments 
• Experience with public sector datasets or analytical workflows 

What can we offer you?

• Competitive salary reviewed annually
• Work for a passionate, mission-driven company solving society’s big problems
• Work flexible hours around life commitments with a focus on delivering company value rather than hours worked
• Ability to work remotely (excluding face-to-face Team Meetings and client meetings)
• Training and development opportunities
• 25 days annual leave (plus bank holidays)
• Company pension
• Private medical insurance
• Generous enhanced parental leave policies
• Cycle to work scheme
• Flu Vaccinations,
• Eye Test and contribution towards Glasses for VDU use
• Employee Assistance Programme
  • Mental health and wellbeing support
  • Remote GP access
  • Counselling/therapy
  • Physiotherapy
  • Medical second opinions