Senior Staff Applied ML Engineer - Search Relevance

Every day, millions of people search for products across Shopify's ecosystem. These aren't just queries—they're dreams, businesses, and livelihoods hinging on whether someone finds the perfect vintage jacket or the exact drill bit they need. The Search Relevance team is at the heart of making that magic happen, tackling some of the industry's toughest challenges at scale.

As a Senior Staff Machine Learning Engineer specializing in Search Relevance, you'll be tasked with designing and implementing AI-powered features using the latest LLM advances and vector matching technologies. You’ll work to improve retrieval quality with query rewriting and expansion and drive innovations that align search results with actual buying behavior, impacting millions globally.

Key Responsibilities:

• Designing, implementing, and refining machine learning algorithms that improve the relevance of search results.
• Analyzing large sets of data, such as user interaction logs and search query trends, to identify opportunities in search relevance.
• Collaborate closely with data scientists and the Foundation Models team to productionize SOTA models aiming at improving search relevance.
• Working closely with product managers and other engineering teams to align on the overall goals of the search product and ensure cohesive integration of new features.
• Actively participating in the design, development, and deployment of end-to-end search relevance solutions.
• Solve high-impact users problems within search relevance engineering, offering hands-on technical leadership to other ICs.
• Mentor engineers and data scientists, fostering a culture of innovation and technical excellence.

Qualifications:

• Experience solving high value problems at the organization level
• Expertise in relevance engineering and recommendation systems, with competence in building search backend before.
• End-to-end experience in training, evaluating, testing, and deploying machine learning models at scale.
• Proficiency in Python, C++, shell scripting, and streaming and batch data pipelines.
• Experience with running machine learning in parallel environments (e.g., distributed clusters, GPU optimization).
• Experience with statistical methods, pre-training and post-training models like BERT, and familiarity with Pytorch.
• Demonstrable experience in building better evaluation metrics that drive search relevance improvements.
• Strong mathematical and computer science fundamentals, with a problem-solving mindset that thrives on challenges
• Excellent communication skills and the ability to work in a fast-paced, collaborative environment.
• Ability to anticipate future technical challenges and innovate solutions proactively.