Candidates for this position are preferred to be based in Bangalore, India and will be expected to comply with their team's hybrid work schedule requirements.

Who We Are:

Wayfair believes everyone should live in a home they love. Through technology and innovation, Wayfair makes it possible for shoppers to quickly and easily find exactly what they want from a selection of more than 14 million items across home furnishings, décor, home improvement, housewares, and more. Committed to delighting its customers every step of the way, Wayfair is reinventing the way people shop for their homes – from product discovery to final delivery.

Wayfair’s Search and Recommendations team builds the core platforms and services that allow our customers to discover & buy the products they love. Just last year alone our team has contributed to hundreds of millions in incremental revenue. We do this by leveraging Wayfair’s extensive customer and product data to deliver trusted and valuable recommendations in real-time using custom machine learning models. We productionalize these ML models as microservices and build data pipelines necessary for inference and model training.

The Predictive Search team is focused on enhancing the autocomplete dropdown, one of the most visible components of our site, to better understand customer intent and deliver the most relevant and personalized search results.

As a Staff Engineer, you’ll play a pivotal role in shaping the technical direction for predictive search. You’ll lead initiatives to design and build high-performing, scalable systems that deliver real-time, machine-learning-powered autocomplete suggestions. You will work closely with cross-functional partners, mentor team members, and deliver impactful results that directly enhance customer experience and drive business outcomes. 

We have ambitious goals to improve this search functionality to better understand customer intent and map them to the most relevant destination on site.

What you’ll do

• Architect and Implement: Lead the design and development of robust, scalable systems supporting autocomplete functionality, ensuring high availability and performance.
• Collaborate Cross-Functionally: Work with a broader highly collaborative cross-functional team that includes product managers, engineering leads, data scientists, and analysts.
• Versatile Technology: Work with a variety of technologies, including Java, Spark, Kafka, Elasticsearch, Aerospike, Hadoop, Airflow, RESTful web services, gRPC, Kubernetes. Additionally, you’ll work with various managed GCP offerings like BigQuery, GKE, BigTable, Memstore, Composer and Vertex AI.
• Real-Time Decision Making: Build platforms and services that allow us to make realtime ML powered decisions that improve the customer’s onsite search experience.
• Drive Impact: Deliver direct measurable results for our business and customers through improved search recommendations.
• Technical Leadership: Mentor junior engineers to develop the next generation of Wayfair engineering.Provide high quality code and technical design reviews.Contribute to the code base, with a mind to best practices and an equally high degree of autonomy.

What you’ll need

• A Bachelor’s Degree in Computer Science, Data Science, or a related engineering discipline.
• 10+ years of professional experience in software engineering, with expertise in building large-scale, distributed systems.
• Knowledge of scalable distributed systems with deep understanding of object oriented design and design patterns.
• Knowledge of designing Java-based APIs and microservices. Strong experience with Spring or Webflux.
• Experience productionizing customer-facing Data Science models, including high throughput, low latency serving layers like ONNX, model-training & feature serving
• Deep understanding of information retrieval technologies (e.g., search engines like Elasticsearch, Solr, Vespa or other Vector-Search Engines).
• Experience working on cloud technologies like Google Cloud Platform, AWS, Azure or other cloud providers
• Experience with customer-level personalization, scoring and ranking via model-driven inference
• Strong understanding of search and recommendation system models is highly desirable.
• Experience using Kubernetes, Docker, Buildkite, and Terraform for containerization and CI/CD.
• Excellent communication skills and ability to work effectively with engineers, product managers, data scientists, analysts and business stakeholders.

Why You’ll Love This Role:

• Work on a highly visible, impactful product at the core of the customer journey.
• Lead cutting-edge ML and search-related innovations in e-commerce.
• Influence the technical strategy and roadmap of a mission-critical system.
• Be part of a collaborative, high-performing team that values continuous learning and improvement.