Machine Learning Engineer

• Build agentic document-understanding systems that search, parse and visually inspect technical files
• Develop agent harnesses to retrieve information across large document collections in varied formats
• Preserve source attribution and minimise hallucination while balancing accuracy, latency and cost
• Deploy agent systems to production
• Define success with assessors and build datasets and benchmarks for a complex domain
• Measure retrieval quality, citation correctness, expert agreement, assessor effort, assessment quality, customer experience and rework
• Use evaluation results to prioritise improvements
• Build agents that respect the impartiality and objectivity required of a certification body
• Help users understand evidence, recognise uncertainty and retain responsibility for consequential judgments
• Own ML systems from conception and prototyping through deployment, evaluation and iterative improvement
• Collaborate with clinicians, assessors and engineers to accelerate medical-device certification without compromising safety

• Partner directly with software engineering and product design to understand requirements
• Design and run model evaluations
• Integrate optimised workflows into production pipelines
• Ensure results remain reliable at scale
• Architect agentic systems for large-scale analysis of the geopolitical landscape
• Train and evaluate domain-specific models for customer problems
• Work with design and engineering to bring models into the product
• Translate research into practical solutions for analysts alongside domain experts
• Lead technical direction on major initiatives
• Set standards across the team and mentor others
• Take projects from prototype through reliable production deployment
• Collaborate with engineers designing the data collection and processing backend
• Engage directly with senior stakeholders
• Make technical decisions on model selection, training approaches, evaluation and deployment
• Deliver production-ready systems at scale
• Uphold standards for architecture, code quality, evaluation and research practice

Senior Machine Learning Engineer

• Contribute to designing systems that balance research velocity with product and production constraints
• Shape technical decisions on model selection, training approaches, evaluation, and deployment for well-defined components
• Build and ship production-ready components combining research rigour with pragmatic engineering
• Take projects from early exploration through to reliable deployment
• Apply high standards for architecture, code quality, evaluation, and research practices
• Work with software engineering and product design to understand requirements
• Contribute to agentic systems for large-scale analysis of the geopolitical landscape
• Train and evaluate domain-specific models
• Translate research into practical solutions for analysts
• Own well-defined components end-to-end and collaborate on broader technical decisions alongside senior engineers
• Participate in an interview process consisting of an initial intro interview, technical assessment, system design assessment, and founder interview

• Design, code, test, and debug ML applications
• Improve large-scale global systems responding to millions of real-time requests per second
• Run machine learning experiments to test new modeling ideas
• Collaborate with senior scientists and engineers to iterate on ML models
• Write clean, efficient, and maintainable code using industry best practices
• Participate in code reviews and provide constructive feedback
• Identify performance bottlenecks and optimize system components for scalability
• Keep up to date with developments in machine learning outside the company
• Bridge academic concepts with industrial-scale systems through mentorship

• Contribute to the design and evolution of the Data Science platform
• Help define best practices, tooling, and the ML Engineering function
• Automate the end-to-end data science lifecycle using CI/CD and infrastructure as code
• Support scalable, enterprise-grade production workflows
• Collaborate across the data science and deployment lifecycle for traditional machine learning and generative solutions
• Work with data engineers, software engineers, and business stakeholders
• Write high-quality Python code for model development, deployment, and maintainability
• Contribute to data science modelling and project workflows
• Help select modelling approaches
• Participate in architecture discussions and deployment strategies
• Transition research models into scalable, production-ready solutions

• Build and maintain machine learning solutions in production across the Consumer business within Lloyds Banking Group
• Develop and maintain machine learning systems in Python alongside data scientists and other machine learning engineers
• Take projects from experimentation through to production
• Design and integrate solutions with continuous integration and continuous delivery tools, application programming interfaces, and Dynatrace
• Engage with business and technical collaborators to understand requirements and influence decision-making
• Propose, evaluate and advocate for technology, platform and architecture choices in line with Lloyds Banking Group guidelines
• Drive engineering standards, consistency and culture through technical leadership and knowledge sharing
• Lead incident management and resolution with the strategic platform team and business partners
• Identify opportunities to improve solutions and present clear delivery plans
• Deliver results aligned with data science, model governance, and risk management policies and procedures
• Build relationships with specialized colleagues across data science, governance and risk management

• Reproduce and evaluate research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring methods, baselines, calibration tests, and experiment harnesses
• Work with model weights, logits, hidden states, activations, model APIs, and inference infrastructure
• Build and extend evaluation infrastructure, including runners, judges, persistence, experiment orchestration, and reporting
• Turn research workflows into product experiences, including experiment configuration, runs, traces, comparisons, reports, and review workflows
• Investigate verification methods under fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion
• Design controlled experiments that separate meaningful signals from artifacts or confounders
• Write technical reports distinguishing measured evidence, interpretation, and hypotheses
• Ship production-quality systems with APIs, background jobs, observability, testing, and documentation
• Reproduce a published model-provenance or verification method within six months
• Build a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports
• Add a verification workflow to Construct and make it accessible through the Eldros UI
• Run controlled experiments across base, fine-tuned, merged, quantized, and distilled models
• Improve understanding of when verification methods succeed, fail, and why

Applied ML Engineer

• Lead development and deployment of cutting-edge AI systems for diverse clients
• Lead technical scoping and architectural decisions for high-impact machine learning systems
• Design and build production-grade ML software, tools, and scalable infrastructure
• Define and implement best practices for deploying machine learning at scale
• Collaborate with engineers, data scientists, product managers, and commercial teams on client challenges and opportunities
• Advise customers and partners by translating complex technical concepts into actionable strategies
• Mentor junior engineers and shape engineering culture and technical depth