Machine learning engineer (python) - remote

At Rockstar Games, we create world-class entertainment experiences. Become part of a team working on some of the most rewarding, large-scale creative projects to be found in any entertainment medium - all within an inclusive, highly-motivated environment where you can learn and collaborate with some of the most talented people in the industry.

Rockstar

Games is on the lookout for a skilled Senior Machine Learning Engineer with strong software development skills who is passionate about games, big data and Machine Learning to join a team that builds Data Science products that directly influences game design, live operations, and player engagement at scale. This is a full-time, in-office position based out of Rockstar's newly renovated game development studio in Andover, MA.

The Rockstar Games

Analytics team provides insights and actionable results to a wide variety of stakeholders across the organization in support of their decision making. We partner with multiple departments across the company, leveraging analytics to measure and improve on the success and health of our games. We collaborate as a distributed team to develop innovative data pipelines, data products, data models, reports, analyses, and machine learning applications.

The Machine Learning

Engineering vertical within the Analytics team is tasked with designing, building, and deploying ML systems in addition to advising other verticals on how to design and build reliable, scalable and, fit for purpose models. Partner with Data Scientists and business stakeholders to understand analytical & ML needs and translate them into robust ML solutions that enable us to leverage and derive insights. Design and build end-to-end ML pipelines, including data, features, training and, serving.

Push the boundaries of our ML and Data Science platform by taking advantage of and spearheading cutting-edge advancements in AI and Agentic frameworks. Perform timely Root Cause Analysis to troubleshoot model and data-related issues; assist in implementation of code and process fixes. 5+ years of experience building ML systems in production. Bachelor’s degree or equivalent in an engineering or technical field such as Computer Science, Mathematics, Statistics, or strong quantitative and software background.

Proven track record in building, monitoring, and optimizing large-scale ML solutions and infrastructure.

Experience working in Databricks and Databricks MLflow is essential.

Experience working with pipeline scheduling tools such as Airflow & Astronomer. Production experience deploying Databricks Genie AI and other Databricks Agentic solutions If we would like to move forward with your application, a Rockstar recruiter will reach out to you to explain next steps and guide you through the process. In line with this commitment, Rockstar will provide reasonable accommodations to qualified job applicants with disabilities during the recruitment process in order for such applicants to be considered for the position for which they are applying, as well as to qualified employees to enable them to perform the essential functions of their roles.

If you need more information about Rockstar’s reasonable accommodation policies or process, or need to request an accommodation, please notify your recruiter during the interview process. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, race, or any other protected category. #Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards, in addition to a full range of medical, financial, and/or other benefits. If hired, employee will be in an "at-will position" and the company reserves the right to modify base salary (as well as any other discretionary payment or compensation or benefit program) at any time, including for reasons related to individual performance, company or individual department/team performance, and market factors.

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