Staff Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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. Core 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., 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
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.
Your Skills & Expertise
- 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.
Who You Are
- 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.
Bonus Points
- Direct experience applying AI/ML to retail or e-commerce workflow automation.
- Experience building systems that involve multiple interconnected ML models or autonomous agents.