MLOps Engineering Manager

MLOps Engineering Manager

Team: Engineering & Data

Location: Depop - London

Company Description

Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly-owned subsidiary of Etsy. Find out more at www.depop.com Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have. If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to . For any other non-disability related questions, please reach out to our Talent Partners.

Life is about creating. That's why we're home to over 30 million artists, stylists, designers, sneakerheads — and you? We're the community-powered, circular-minded marketplace changing the world of online fashion. Now it's time to get inspired at Depop.

Responsibilities

Job description

We are looking for an experienced MLOps Manager to lead our growing MLOps function. You’ll manage a team of 8 engineers and specialists, set the strategic direction for how machine learning is developed, deployed, and scaled across Depop, and ensure we are delivering robust, secure, and scalable ML infrastructure.

Our MLOps stack is modern, cloud-native, and carries very little tech debt, giving us the freedom to focus on building rather than firefighting. This role comes with a wide span of control and genuine strategic agency — you’ll be shaping the direction of how machine learning is built, deployed, and scaled across the business. Because Depop is deeply ML-driven, this position isn’t just about keeping the lights on; it’s central to how we deliver value to our users and to the business. You’ll be empowered to set best practices, influence architectural decisions, and ensure that our teams can move quickly while maintaining trust, reliability, and impact.

This role combines technical leadership, vendor management, and strategic oversight, and will require you to work closely with engineering, product, and data leadership to ensure machine learning drives real impact for Depop and our community.

Key Responsibilities

Team Leadership: Lead, coach, and develop a team of 8 MLOps engineers, fostering a culture of high performance, collaboration, and growth.

Technical Leadership & Decision-Making: Provide guidance on architecture, tooling, and best practices for ML pipelines, model deployment, monitoring, and incident management.

Collaboration with Applied ML Org: Partner closely with machine learning scientists, ML management, and cross-functional teams across the organisation to ensure MLOps solutions are aligned with scientific innovation, business priorities, and operational needs.

Strategic Direction: Define and deliver the MLOps strategy, aligning with Depop’s broader Data and Engineering roadmap.

Vendor Management: Oversee operational relationships with external vendors, ensuring service quality, cost-effectiveness, and alignment with team needs. Collaborate ​​with Vendor Managers on contract negotiations, renewals, and vendor evaluations to ensure optimal long-term partnerships.

Leadership Reporting: Provide clear, concise updates on progress, risks, and priorities in fortnightly leadership updates.

Incident Oversight: Act as an escalation point for ML/infra incidents, ensuring rapid resolution and learnings captured.

Cross-Org Collaboration: Engage proactively with the Data org (data science, data engineering, analytics) to ensure MLOps solutions meet evolving business needs.
 

Requirements

Proven experience leading an MLOps, ML Engineering, or Platform Engineering team.

Deep-rooted enthusiasm for Platform disciplines

A background in Applied Machine Learning

Hands-on experience with cloud platforms (AWS, GCP, or Azure), including provisioning and managing ML infrastructure at scale.

Strong understanding of GPU computing for training and serving ML models.

Experience designing and managing containerised ML workloads (Docker, Kubernetes, Kubeflow, or similar orchestration frameworks), and integrating with CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI).

Familiarity with distributed data processing and ML training frameworks (e.g., Spark, Ray, TensorFlow Distributed, or PyTorch Distributed).

Solid grasp of monitoring, logging, and observability for large-scale ML systems.

Knowledge of cost optimisation strategies for compute- and GPU-intensive workloads in cloud environments.

Excellent people leadership skills, with experience managing and developing engineering teams.

Demonstrated ability to set strategy and drive execution in a fast-paced environment.
Strong communication skills, with the ability to provide clear updates to technical and non-technical audiences.

Proactive, collaborative, and comfortable engaging across multiple disciplines.
 

Nice to haves

Experience managing vendors, contracts, and budgets.

Experience with Databricks, Tecton (or Feast), Seldon, SageMaker