Nike is actively recruiting software engineers to bolster its Data & AI (DAI) team, signaling a continued investment in leveraging artificial intelligence across its operations, according to a recent job posting. The company seeks individuals with experience in cloud platforms, data pipelines, and modern data architectures.
The role, advertised internally, focuses on the design, development, and maintenance of scalable data solutions. Nike is specifically looking for engineers proficient in languages like Python, Java, C#, or SQL, and experienced with tools such as Databricks, Snowflake, and Apache Spark. Expertise in infrastructure-as-code tools like Terraform or AWS CloudFormation is also considered critical.
This recruitment drive comes as Nike works to regain momentum in digital sales and overall revenue, a process that has involved strategic shifts in leadership and a renewed focus on AI-driven initiatives. Elliott Hill took over as Nike’s CEO in 2024, followed by changes in the leadership overseeing AI and machine learning, and the retirement of the chief innovation officer. Despite these transitions, the company reported year-over-year revenue growth in its fiscal Q1 2026, ending August 31, for the first time since fiscal Q3 2024.
Nike’s AI strategy extends to multiple facets of its business, including customer experience, brand representation, and product optimization. In August, Muge Erdirik Dogan, Nike’s executive vice president and chief technology officer, announced the launch of NikeAI Beta for iOS Nike App users in the U.S., an AI-powered search and recommendation tool designed to handle natural language queries. Users can now request products based on specific needs, such as “running shoes for a race” or “gear for your team,” receiving personalized recommendations in response.
Beyond customer-facing applications, Nike is utilizing AI to improve product development. The Nike Fit app, employing computer vision and machine learning, scans feet to create 3D models, aiming to reduce return rates caused by sizing inaccuracies. Data collected through Nike Fit also informs broader design analytics, allowing the company to tailor products to regional preferences.
The company’s AI efforts also include leveraging AI chatbots for personalized product recommendations, as demonstrated through partnerships with platforms like Naver’s HyperCLOVA X in South Korea. These chatbots have reportedly achieved a 20% higher click-through rate compared to traditional advertising methods. Integration with Shopify’s AI tools further enhances product discoverability through natural language search.
While Nike has made significant investments in AI, challenges remain. Data privacy is a key concern given the volume of customer data collected through various apps. Maintaining the quality of 24/7 customer service through AI-powered chatbots also presents ongoing hurdles.
The DAI team’s focus on Lakehouse architecture, Data Mesh principles, and data governance frameworks underscores Nike’s commitment to building a robust and scalable data infrastructure to support its expanding AI initiatives. The team will be responsible for ensuring data quality and reliability through rigorous testing and validation processes.
