Staff Machine Learning Engineer - Seattle

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 Staff Applied Scientist/Machine Learning Engineer

Compensation: $235,000 - $300,000, employee equity plan grant & world class benefits.

As a Senior/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.