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- Tasks: Join us to design and maintain ML tooling and platforms for seamless model deployment.
- Company: Depop is a vibrant marketplace where machine learning drives innovation and value.
- Benefits: Enjoy flexible working options, a supportive culture, and opportunities for professional growth.
- Why this job: Be part of a dynamic team that values technical innovation and continuous improvement.
- Qualifications: Strong Python skills and experience with ML libraries and cloud platforms are essential.
- Other info: Collaborate closely with ML Scientists and Backend Engineers to maximise impact.
The predicted salary is between 36000 - 60000 £ per year.
At Depop, machine learning is integral to our value proposition. We are looking for an MLOps Engineer to help level-up how ML solutions are delivered at Depop. You will be responsible for enabling our ML Scientists - currently spread across five product functions - to deliver value by providing self-serve platforms and services for ML model + feature development, deployment and monitoring.
Do you find happiness in providing tooling, services and platforms that help businesses untap the enormous value of machine learning? If so, this could be the perfect match.
Responsibilities:
- Helping design, implement and maintain tooling + platforms for:
- Productionising model training workflows
- ML feature engineering and deployment
- Deploying, monitoring and managing ML models in production
- Model performance monitoring and drift detection
- Model retraining, rollback, and continuous improvement
Requirements:
- Consistent track record of successful end-to-end delivery of your projects; from scoping and translating business/user requirements into plans, to design, implementation and maintenance, whilst coordinating with other teams (and engineers).
- Strong programming skills in Python, with experience in ML libraries such as TensorFlow, PyTorch and Scikit-learn.
- Experience working with ML training/inference platforms such as Databricks, SageMaker and Seldon.
- Experience building CI/CD processes with tools such as Jenkins or GitHub Actions.
- A strong sense of ownership, autonomy and a highly organised nature.
- Exemplary communication skills, especially in dealing with multiple stakeholders.
- Proficiency with cloud platforms (e.g., AWS, GCP, Azure) and containerization (Docker, Kubernetes).
ml Ops Engineer employer: Depop
Contact Detail:
Depop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ml Ops Engineer
✨Tip Number 1
Familiarise yourself with the specific ML tools and platforms mentioned in the job description, like TensorFlow, PyTorch, and Databricks. Having hands-on experience or projects showcasing your skills with these technologies can set you apart from other candidates.
✨Tip Number 2
Network with current or former employees of Depop, especially those in engineering roles. Engaging in conversations about their experiences can provide valuable insights into the company culture and expectations, which you can leverage during interviews.
✨Tip Number 3
Demonstrate your understanding of MLOps by discussing real-world scenarios where you've implemented CI/CD processes or managed ML models in production. Be prepared to share specific examples that highlight your problem-solving skills and technical expertise.
✨Tip Number 4
Showcase your communication skills by preparing to discuss how you've collaborated with cross-functional teams in the past. Highlighting your ability to work closely with ML Scientists and Backend Engineers will illustrate your fit for the enablement role described in the job listing.
We think you need these skills to ace ml Ops Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, machine learning, and the specific tools mentioned in the job description, such as Python, TensorFlow, and cloud platforms. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how you can contribute to Depop's goals. Mention specific projects or experiences that demonstrate your ability to deliver ML solutions and work collaboratively with teams.
Showcase Your Projects: If you have worked on relevant projects, consider including a portfolio or links to your GitHub. Highlight any end-to-end delivery of ML projects, CI/CD processes, or experience with monitoring and managing ML models in production.
Prepare for Technical Questions: Anticipate technical questions related to MLOps, programming in Python, and the tools listed in the job description. Be ready to discuss your problem-solving approach and how you've handled challenges in previous roles.
How to prepare for a job interview at Depop
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and ML libraries like TensorFlow, PyTorch, and Scikit-learn. Bring examples of past projects where you successfully implemented these technologies, as this will demonstrate your technical proficiency.
✨Understand the Role of MLOps
Familiarise yourself with the MLOps lifecycle, including model training, deployment, monitoring, and retraining. Be ready to explain how you can enhance the efficiency of ML Scientists by providing effective tooling and platforms.
✨Communicate Clearly with Stakeholders
Exemplary communication skills are crucial for this role. Practice articulating complex technical concepts in a way that is understandable to non-technical stakeholders, as you'll need to collaborate closely with various teams.
✨Demonstrate a Culture of Continuous Improvement
Depop values innovation and professional development. Share examples of how you've contributed to a culture of continuous improvement in your previous roles, whether through process enhancements or personal learning initiatives.
