Senior Machine Learning Engineer - Search (REMOTE)

At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams.  We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.

If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!

OVERVIEW:

Are you a passionate technologist with experience in AI, Machine Learning, Data  Science and Analysis? Are you looking for an opportunity to drive enterprise impact and shape the future of a leading sports retailer with $12B+ in revenue and 800+ physical stores? Do you enjoy working with a highly skilled team of Machine Learning engineers & Scientists, co-creating enterprise grade AI capabilities?

As a Senior Machine Learning Engineer (Search), you will be an emerging technical leader in our enterprise-wide transformation, focused on delivering best-in-class search experiences through intelligent, AI-driven systems. You’ll empower teammates and customers by building advanced tools rooted in AI/GenAI and machine learning —enabling smarter, faster, and more personalized discovery across our digital & in-store ecosystem.

JOB PURPOSE

This is a career-defining opportunity to shape how search powers decision-making, productivity, and engagement across the enterprise. From product discovery and internal knowledge retrieval to teammate enablement and operational intelligence, you’ll build scalable ML solutions that elevate every facet of search — across channels, roles, and use cases.

This role requires a seasoned engineer with deep expertise in traditional machine learning algorithms and a strong grasp of cutting-edge AI/GenAI techniques — especially those applied to enterprise-scale search systems. You’ll be responsible for designing and deploying intelligent search infrastructure that spans product discovery, internal knowledge retrieval, teammate enablement, and operational decisioning.

As a technical leader, you’ll shape the enterprise ML/AI landscape by building high performance, deeply integrated systems across frontend interfaces, backend services, data platforms, and search infrastructure. You’ll collaborate closely with product, engineering, and business stakeholders to architect scalable solutions, drive platform evolution, and unlock the full potential of AI through rigorous technical design and hands-on implementation.

RESPONSIBILITIES

• Architect and implement large-scale search and ranking systems.

• Build robust ML pipelines for training, evaluation, and deployment.

• Optimize distributed systems for low latency, high throughput, and fault tolerance.

• Ensure models are production-ready with monitoring, logging, and automated retraining.

• Integrate ML models into search infrastructure (retrieval, ranking, query understanding).

• Work with embeddings, transformers, and vector search technologies to improve relevance.

• Scale experimentation frameworks to support rapid iteration and safe rollouts.

• Design Cloud deployment architecture for deploying ML models as APIs for real-time inference with Caching.

• Develop and maintain APIs for machine learning models to facilitate integration with other systems and applications.

• Implement observability for search metrics (latency, relevance, clickthrough).

• Work closely with the Machine Learning Platform team to develop and maintain the ML platform to meet business and science objectives utilizing cutting edge tools and techniques.

• Partner with product managers and data scientists to translate ideas into production systems.

• Understand latest research in the field of search and give inputs to enterprise roadmaps to ensure we are on the path to build Best in Class search & relevancy systems.

PREFERRED SKILLSET

• Master's Degree or Equivalent Level in quantitative fields like computer science, engineering, physics, mathematics, etc.

• 4+ years of experience in software engineering for ML systems (search, recommendation, or large-scale distributed systems).

• Experience in API engineering, including designing, developing and maintaining APIs.

• Languages & Tools: Python, Java/Scala, C++.

• ML Frameworks: TensorFlow, PyTorch.

• Data & Infra: