Wayfair - Software Engineer II - Machine Learning

Location : 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 is one of the worlds largest destinations for the home, powered by a complex technology ecosystem that enables millions of customers to find the right products, at the right price, delivered seamlessly to their doorsteps.

Pricing and profitability are at the heart of Wayfairs strategy, and the algorithms that support them are critical to ensuring we remain competitive and sustainable.

We are building horizontally scalable systems that power price generation, simulations, and profitability modeling relying on data, experimentation, and machine learning to guide our decisions.

These systems enable us to react quickly to market trends, optimize gross margins, and ensure an outstanding customer experience.

We are looking for passionate backend engineers to help us build and scale these mission-critical systems that drive decisions across pricing, profit, product placement, and customer satisfaction.

This role sits at the intersection of backend engineering, data platforms, and applied machine learning transforming experimental models into reliable, production-ready systems that operate at scale.

Engineers in this space will not only solve complex technical challenges but also directly shape the way Wayfair competes in the global marketplace through smarter pricing and profitability strategies.

What Youll Do :

• Collaborate with Data Scientists and fellow Software Engineers to bring ML models into production.
• Design, build, and maintain backend services and data pipelines that enable data-driven decision-making.
• Write efficient, scalable, and maintainable code in Python, with a focus on high reliability and performance.
• Develop and optimize Google BigQuery (GBQ) / SQL queries, including joins and aggregations across large datasets.
• Ensure production readiness through testing, monitoring, deployment, documentation, and proactive troubleshooting.
• Use orchestration tools (e.g., Cloud Composer) to schedule and manage workflows.
• Support report generation and integrations with tools like Looker Studio or similar.
• Take ownership of projects end-to-end, from design and implementation to scaling and ongoing maintenance.
• Balance the open-ended nature of science-driven experiments with the rigor of production-grade engineering.

We Are a Match Because You Have :

Nice to Have :

(ref : hirist.tech)