Ready for a challenge?
Whether it’s a Friday-night feast, a post-gym poke bowl, or grabbing some groceries, our tech platform connects tens of millions of customers with hundreds of thousands of restaurant, grocery and convenience partners across the globe.
About this role
As Senior ML Engineer in the Customer pillar, you will have the opportunity to build and shape the machine learning solutions that delight millions of customers using our apps worldwide.
You will work closely with data scientists to design and build end-to-end data pipelines on our machine learning platform to support ML models in our customer-facing apps.
You will help drive better restaurant recommendations, balancing the trade offs between relevance, logistic efficiency and quality of service.
Your direct team-mates will be Data Scientists, ML Engineers, Analytics Engineers and Back end API Engineers.
These are some of the key components to the position:
- Design, develop, release and maintain reliable, secure and scalable machine learning solutions in a cloud environment (strong preference for GCP and AWS), preferably using infrastructure as code (e.g. Terraform).
- Build reliable, secure, and scalable data pipelines and production-ready ML pipelines that allow our data scientists to bring ML models to production quickly and responsibly.
- Identify business and technical requirements to help define a robust data extraction & transformation process, collaborating with data science, product, and other technical teams.
- Partner with stakeholders as a subject matter expert on ML infrastructure at scale
- Develop data workflow monitoring and alerting solutions that provide diagnostic & actionable information to stakeholders.
- Ensure delivery of high-quality data engineering solutions,establishing and following best practices for development, testing, and deployment.
- Use agile software development principles, DevOps/SRE, and MLOps practices to facilitate a bigger data science impact.
- Work cross-functionally with Analytics Engineering, ML Engineering, and DevOps to resolve issues and standardise practices.
- Guide and coach data scientists and junior ML Engineers to help them develop their engineering skills.
- Be an active part of the Best Practice Ambassador community including senior data scientists & ML engineers
What will you bring to the team?
- Strong data/ML engineering experience preferably in machine learning-related projects.
- Solid knowledge of Python for ML engineering applications as evidenced by earlier work in data/ML/software engineering. Able to confidently write elegant source code with minimal supervision
- Excellent SQL skills.
- Demonstrable experience designing, building and orchestrating ML pipelines with tools like Airflow
- Comfortable working with different technologies across the software and machine learning stack for data transformation, model training (e.g. Sagemaker, Vertex AI), automation (e.g. GitHub Actions, Jenkins) and monitoring.
- Familiar with data validation practices and standards (e.g. data testing, quality monitoring, etc).
- Knowledge of software engineering best practices across the product development lifecycle, including coding standards, code reviews, pair programming, build processes, testing, operations, and CI/CD.
- A caring attitude towards the personal and professional development of the wider team with an ability to upskill more junior members of the team through pairing and reviews.
At JET, this is on the menu:
Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment.
Fun, fast-paced and supportive, the JET culture is about movement, growth and about celebrating every aspect of our JETers. Thanks to them we stay one step ahead of the competition.
Inclusion, Diversity & Belonging:
What else are we delivering?
