EDITED - Staff Machine Learning Engineer
Requirements
• 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. • Direct experience applying AI/ML to retail or e-commerce workflow automation. • Experience building systems that involve multiple interconnected ML models or autonomous agents.
Responsibilities
• 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. • Product-Minded Engineering • 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.
Benefits
• We value our team and to attract exceptional people, we offer an excellent package. • You can utilise our flexible working policy to ensure you can work around your schedule - this means starting & finishing when it suits you best! • At EDITED we are set up to work remotely and utilise a hybrid approach in our central London office • Enhanced parental leave policy • 25 days annual leave + public holidays (and an extra day for every year at EDITED) • Work from anywhere policy • Season Ticket Loan & Cycle to Work schemes • Health Cash App • Access to an Employee Assistance Programme • Gifts for work anniversaries and big life events • Dog friendly office • Find out more about working at EDITED here: https://edited.com/careers/ • We aim to be an equal opportunities employer and we are determined to ensure that no applicant or employee receives less favourable treatment on the grounds of gender, age, disability, religion, belief, sexual orientation, marital status, or race, or is disadvantaged by conditions or requirements which cannot be shown to be justifiable.
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