Role based in Sydney, Australia - Relocation Support Provided

We are Rokt, a hyper-growth ecommerce leader. We enable companies to unlock value by making each transaction relevant at the moment that matters most, when customers are buying. Together, Rokt's AI-based relevance Platform and scaled ecommerce Network powers billions of transactions. In December 2022, Rokt’s valuation increased to $2.4 billion USD, allowing us to expand rapidly across 15 countries.

The Rokt engineering team builds best-in-class ecommerce technology that provides personalized and relevant experiences for customers globally and empowers marketers with sophisticated, AI-driven tooling to better understand consumers. Our bespoke platform handles millions of transactions per day and considers billions of data points which give engineers the opportunity to build technology at scale, collaborate across teams and gain exposure to a wide range of technology.

At Rokt, we practice transparency in career paths and compensation.

At Rokt, we believe in transparency, which is why we have a well-defined career ladder with transparent compensation and clear career paths based on competency and ability. Rokt’stars constantly strive to raise the bar, pushing the envelope of what is possible.

We are looking for a Senior Machine Learning Engineer

Compensation: $190,000-$235,000 AUD including superannuation, plus employee equity plan grant & world class benefits.

As a Senior Machine Learning Engineer, you are someone who has significant expertise in both machine learning and software engineering. You will be working with our engineering and product teams to design, build and productionise proprietary machine learning models to solve different business challenges including smart bidding, lookalike modelling, forecasting, etc.

About the Role:

• Collaborate closely with product managers and other engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions.
• Build and productionise machine learning models including data preparation/processing pipelines, machine learning orchestrations, improvements of services performance and reliability and etc.
• Contribute and maintain the high quality of the code base with tests that provide a high level of functional coverage as well as non-functional aspects with load testing, unit testing, integration testing, etc.
• Keep track of emerging tech and trends, research the state-of-art deep learning models, prototype new modelling ideas, and conduct offline and online experiments.
• Share your knowledge by giving brown bags, tech talks, and evangelising appropriate tech and engineering best practices.