EDITED is the world’s leading AI-driven retail intelligence platform. We empower the world’s most successful brands and retailers with real-time decision making power.
By connecting internal business and external market data, EDITED infuses intelligence into every retail decision. We help retailers increase margins, generate more sales, and drive better business outcomes through AI-powered market and enterprise intelligence that fuels automation.
At EDITED, we foster a dynamic and inclusive culture where creativity thrives and collaboration is at the heart of everything we do. Our environment is dynamic and supportive, encouraging team members to take initiative, innovate, and continuously grow. We value diversity, transparency, and a shared commitment to excellence, creating a workplace where everyone's voice is heard and contributions are recognised.
We believe that achieving a positive work-life balance is key to driving innovation and success. Our flexible working options—including hybrid working, flexible hours and a work from anywhere policy—empower our team to perform at their best.
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.
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.
Direct experience applying AI/ML to retail or e-commerce workflow automation.
Experience building systems that involve multiple interconnected ML models or autonomous agents.
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.