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Senior Machine Learning Engineer

What You'll Do

At SuperAwesome, a Machine Learning Engineer’s primary function is to provide support for our engineers that are using machine learning to extract value from the increasing amount of contextual data that we receive in the KidTech space. In this role you’re going to take engineering and ML research techniques and apply them to real world problems, overcoming the obstacles associated with real world data along the way.

The ideal candidate is somebody who has a natural drive to solving challenging problems and won’t give in at the first hurdles. Your days will see lots of coding, analysing and extending our data and using current best practices to produce, iterate on and productionise ML models. You’ll be supported by a cross-functional team on the same mission: making the internet safer for kids, as well as by a powerful technology stack and a broad toolbox of established industry best practices.

Our Machine Learning Platform is Kubeflow and we use the full stack of Pytorch, Jupyter Notebooks, Kubeflow Pipelines and KServe.
We don’t expect you to know our exact stack and you’ll be given training and help during your onboarding to become fully proficient with it.
We expect you to already have a strong engineering background that has translated well to the Machine Learning space and experience in deploying production quality ML models.

In This Role You Will

• Work on and improve our Machine Learning platform and processes
• Deploy classifiers, models, algorithms and tools to solve real world problems affecting kids’ online data privacy
• Work closely with Product Teams and the Data Team to continuously iterate and improve our model metrics
• Support people from different backgrounds and roles: Product Managers, Engineers, Customer Success, Sales, investigating how machine learning can provide them with valuable insights
• Support machine learning and data driven work in product teams across a variety of domains in the kids space

What We're Looking For

• Experience deploying scalable classifiers and models to production environments
• Experience working with a programming language of choice (e.g Python) to write high-quality data processing code
• Strong systems-level debugging
• Good understanding of services lifecycle and development practices
• Experience with SQL and/or NoSQL databases

Nice To Have Skills

• Experience training machine learning and deep learning models on a computing cluster
• Experience with NLP and NLC
• Experience building REST APIs for analytics services
• Familiarity writing and optimising advanced SQL queries
• Good understanding of data pipelines in production e.g. Airflow, Spark
• Experience with Kubeflow
• Good understanding of Docker, Kubernetes and Terraform
• Experience working with big data and data warehousing solutions e.g. Snowflake, BigQuery