• Own the Outcome by leading features end-to-end • Put User Experience First and optimise for minimal friction • Champion simple implementations and fight complexity • Ship small changes frequently and use short feedback loops • Partner with Product and Science teams to break down complex problems

• Lead the design, development, and deployment of enterprise AI/ML solutions • Collaborate with business and engineering teams to define and prioritize AI projects • Drive rapid proof-of-concept development for AI initiatives • Mentor your team and foster continuous learning • Take ownership of ML projects with state-of-the-art solutions • Research and design new solutions and complex architectures • Proactively propose new tools, solutions, and methodologies • Ensure project scalability and robustness • Manage cross-team communication and planning • Participate in team hiring

Role Description We are looking for a Senior Software Engineer to join the Ember team and help us build the robust backend systems and APIs that power these product experiences, while also contributing to fullstack development when needed. This is a hands-on role for someone who enjoys writing high-quality code and contributing to reliable, well-observed systems in production. Qualifications - Strong experience building backend systems and APIs in production environments. - Experience writing software in TypeScript, Java. - Proficiency with GCP and Terraform. - A track record of owning work from initial design through to production support. - Experience working cross-functionally with engineers, product owners, and designers to deliver valuable outcomes. - Experience in regulated/security-sensitive environments or building financial/accounting systems is a plus. - Frontend experience (React, TypeScript) is a plus. Requirements - Own the design, implementation, and maintenance of backend services and product functionality. - Contribute to the design of scalable, reliable systems with a strong focus on simplicity and maintainability. - Build and improve services running in GCP, using Terraform to support infrastructure changes. - Improve observability across our systems, including monitoring, alerting, and logging. - Support high engineering standards through code review, technical discussion, and mentoring other engineers. Benefits - 25 days holiday (plus take your public holiday allowance whenever works best for you). - An extra day’s holiday for your birthday. - Annual leave is increased with length of service, and you can choose to buy or sell up to five extra days off. - 16 hours paid volunteering time a year. - Salary sacrifice, company enhanced pension scheme. - Life insurance at 4x your salary & group income protection. - Private Medical Insurance with VitalityHealth including mental health support and cancer care. - Generous family-friendly policies. - Perkbox membership giving access to retail discounts, a wellness platform for physical and mental health, and weekly free and boosted perks. - Access to initiatives like Cycle to Work, Salary Sacrificed Gym partnerships and Electric Vehicle (EV) leasing.

• Design and implement QA foundations and evaluation frameworks for assessing AI agents, their workflows and processes. • Design and run experiments to assess AI-driven QA processes, including prompt strategies, automation workflows, and agent performance. • Benchmark systems across multiple engineering streams to determine accuracy, reliability, cost, and consistency of outputs. • Develop approaches for creating synthetic datasets and structured test data to support reliable evaluation. • Identify gaps in existing testing approaches and design AI-based solutions or agents to address them. • Build proof-of-concepts (POCs) to explore new testing approaches and validate ideas. • Structure and document product knowledge, including user journeys, APIs, and business rules, to support AI-assisted testing systems. • Evaluate the cost, ROI, and effectiveness of different QA approaches and automation strategies. • Collaborate closely with the QA Lead and engineering teams to integrate AI-assisted QA into development workflows. • Ensure AI-driven testing processes produce consistent, reliable, and high-quality results. • Remain hands-on in building tooling, experiments, and testing infrastructure.