Staff Machine Learning Engineer, EDITED

• 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
• Outcome over Output: You prioritise delivering a working solution that solves a business challenge over writing "perfect" but impractical code
• 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
• 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
• Agentic AI: Hands-on experience with modern AI orchestration tools such as LangChain and LangSmith
• Production Excellence: Proven experience with Docker, Kubernetes, and cloud infrastructure (AWS/GCP/Azure), with a focus on scaling models in production
• Data Fluency: Expert-level SQL/NoSQL skills and the ability to design high-performance pipelines for massive datasets
• Academic/Practical Background: A Master’s or PhD in Computer Science or a related field, or equivalent experience leading research-heavy engineering projects
• 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."
• A Pragmatic Problem Solver: You value theoretical excellence but prioritise the delivery of scalable, reliable systems that move the needle for the business
• A Data-Driven Thinker: You rely on empirical evidence and rigorous metrics to evaluate models and inform your architectural decisions
• 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

Desirable

• As a Staff Machine Learning Engineer, you will be a driving force behind our AI strategy, moving beyond simple models to build complex, production-ready AI agents and scalable systems
• You won’t just be prototyping; you will take full ownership of the ML lifecycle—from initial data exploration to architecting the MLOps pipelines that keep our models performing at their peak
• This is a high-impact role where you will bridge the gap between cutting-edge research and pragmatic engineering, specifically focusing on automating complex business workflows within our retail and e-commerce ecosystem
• End-to-End Engineering: Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges
• MLOps Ownership: Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerisation practices
• Architectural Leadership: Build high-performance data pipelines (ETL/ELT) for both training and real-time inference, ensuring our systems are scalable and reliable
• Technical Mentorship: Act as a technical lead for the team, mentoring junior engineers, setting engineering best practices, and shaping our long-term technical roadmap
• Cross-Functional Collaboration: Partner with Product Managers and Data Scientists to translate business ambitions into sophisticated technical requirements
• At EDITED we are set up to work remotely and utilise a hybrid approach in our central London office

• CV call with a member of our People Team
• Video call with the Director of Data Science
• Technical interview with the team
• Final interview with CTO