Staff Machine Learning Engineer

Role overview

• check_circle End-to-End Engineering: Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges.
• check_circle MLOps Ownership: Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerisation practices.
• check_circle Architectural Leadership: Build high-performance data pipelines (ETL/ELT) for both training and real-time inference, ensuring our systems are scalable and reliable.
• check_circle Technical Mentorship: Act as a technical lead for the team, mentoring junior engineers, setting engineering best practices, and shaping our long-term technical roadmap.
• check_circle Cross-Functional Collaboration: Partner with Product Managers and Data Scientists to translate business ambitions into sophisticated technical requirements.
• check_circle User-Centric Focus: You don't just build models for the sake of complexity; you build them to solve specific problems for our customers and internal teams.
• check_circle Outcome over Output: You prioritise delivering a working solution that solves a business challenge over writing "perfect" but impractical code.
• check_circle Iterative Discovery: You are comfortable working in the "grey area," using data and user feedback to refine your technical approach as the problem becomes clearer.
• check_circle ML Fundamentals: Strong proficiency in Python and frameworks like PyTorch, TensorFlow, or Scikit-learn, with a deep understanding of NLP, deep learning, or reinforcement learning.
• check_circle Agentic AI: Hands-on experience with modern AI orchestration tools such as LangChain and LangSmith.
• check_circle Production Excellence: Proven experience with Docker, Kubernetes, and cloud infrastructure (AWS/GCP/Azure), with a focus on scaling models in production.
• check_circle Data Fluency: Expert-level SQL/NoSQL skills and the ability to design high-performance pipelines for massive datasets.
• check_circle Academic/Practical Background: A Master’s or PhD in Computer Science or a related field, or equivalent experience leading research-heavy engineering projects.
• check_circle A Proactive Owner: You don’t wait for permission to fix a bottleneck; you take full responsibility for the health of your models from "code to customer."
• check_circle A Pragmatic Problem Solver: You value theoretical excellence but prioritise the delivery of scalable, reliable systems that move the needle for the business.
• check_circle A Data-Driven Thinker: You rely on empirical evidence and rigorous metrics to evaluate models and inform your architectural decisions.
• check_circle A Collaborative Leader: You can explain complex AI concepts to a non-technical stakeholder just as easily as you can conduct a deep-dive code review with a peer.
• check_circle Direct experience applying AI/ML to retail or e-commerce workflow automation.
• check_circle Experience building systems that involve multiple interconnected ML models or autonomous agents.

Benefits

• check_circle You can utilise our flexible working policy to ensure you can work around your schedule - this means starting & finishing when it suits you best!
• check_circle At EDITED we are set up to work remotely and utilise a hybrid approach in our central London office
• check_circle Enhanced parental leave policy
• check_circle 25 days annual leave + public holidays (and an extra day for every year at EDITED)
• check_circle Work from anywhere policy
• check_circle Season Ticket Loan & Cycle to Work schemes
• check_circle Health Cash App
• check_circle Access to an Employee Assistance Programme
• check_circle Gifts for work anniversaries and big life events
• check_circle Dog friendly office

About the company