Senior Machine Learning Engineer

We are Rokt, a hyper-growth ecommerce leader. We enable companies to increase value by unlocking real-time relevancy in the moment that matters most, when customers are buying. Together, our AI and ML-powered Rokt Brain and ecommerce Rokt Network will power more than 6.5 billion transactions connecting 400 million customers across the world’s leading companies. In January 2025, Rokt’s valuation increased to $3.5 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.

The Role

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.

Target total compensation for this role ranges from $300,000 - $325,000 which includes a fixed annual salary between $200,000-$225,000 (including superannuation) and an employee equity plan grant component. In addition, you will receive world-class employee benefits.

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.

• Masters or PhD in Machine Learning
• Extensive knowledge in and experience with some of the following areas - Bayesian methods, Recommender systems, multi-task modelling, meta-learning, click through rate modelling or conversion rate modelling
• 3+ years of industry experience in building production-grade machine learning systems with all aspects of model training, tuning, deploying, serving and monitoring
• Experience with Kubeflow (or similar), Tensorflow and Feature Store in a production environment is a massive plus.
• Bonus points if you are familiar with any of the following architectures or have experience with the models mentioned in this benchmark: DCNV2, MMOE, Deep & Wide, ESMM, xDeepFM, and GDCN.