Staff Machine Learning Engineer - Seattle

We are Aftersell by Rokt, a hyper-growth Shopify ecommerce leader.

Aftersell by Rokt enables SMB companies to unlock 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. In February 2024, Aftersell was acquired by Rokt.

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 Staff Machine Learning Engineer

Target Total Compensation: $375,000 - $514,000, including fixed annual salary of $250,000 - $315,000, employee equity plan grant & world class benefits.

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

Responsibilities

• Collaborate closely with product managers and other engineers to understand business priorities, frame machine learning problems, architect machine learning solutions.
• Build and productionize machine learning models including data preparation/processing pipelines, machine learning orchestrations, improvements of services performance and reliability, etc
• Contribute and maintain the high quality of 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 modeling ideas, and conduct offline and online experiments.
• Share your knowledge by giving brown bags, tech talks, and evangelizing appropriate tech and engineering best practices.

• Bachelor’s degree in Computer Science, a similar technical field of study or equivalent practical experience. A PhD degree in Machine Learning or Deep Learning is a massive plus.
• 5+ years in developing production-grade machine learning systems, preferably with applied ML in Ads, and 3+ years in software engineering with skills in Python, Golang, Java, or similar languages.
• Experience in the following areas - Bayesian methods, Reinforcement learning, Deep learning Architectures and Recommendation systems and if you have experience in ML for Ads or ecommerce it is a big plus.
• You excel in a dynamic, fast-paced setting, bringing a proven track record of meaningful contributions across teams, while effectively communicating ideas and learnings through collaborative discussions and presentations.
• Experience with Kubernetes, Kubeflow, TFX and Feature Store in a production environment is a massive plus