Senior Machine Learning Engineer

Grailed is looking for a Senior Machine Learning Engineer to drive personalization, recommendation, and product marketplace improvement efforts. This is a high‑impact role for an experienced builder who thrives in a lean, high‑talent environment. You will join a high‑velocity team with significant autonomy in taking products from zero to one.

The ideal candidate is able to think like a Grailed user as well as a business owner – understanding how data impacts a fashion‑forward user experience and also how it is generated and leveraged – while bringing a strong technical background to the role. Specifically, the role requires an understanding of dimension reduction techniques, predictive modeling (statistical or ML), and other advanced analytic methods for applications such as personalization, inventory valuation and search optimization.

This key role will operate at the intersection of Data, Product, Engineering, and Marketing, working cross‑functionally to develop compelling data products to support buyers’ progression through the purchase cycle.

This role will work with our data in Snowflake, develop models in Python, collaborate with ML engineers to structure data for consumption, and coordinate with Product and business unit leaders to align data product development with business objectives.

• Act as a technical lead within the data team to advance our recommendation & search algorithms. Focus on improving the relevance & quality of inventory impressions served to prospective buyers.
• Develop proprietary AI/ML solutions that reflect our unique marketplace dynamics (peer‑to‑peer exchange of second‑hand clothing & accessories that are represented as “one‑of‑one” listings).
• Form a high‑level perspective on objectives across departments and how advanced data methods might solve complex business problems.
• Autonomously and proactively identify business problems that could benefit from data solutions, propose, align, and execute from start to finish.
• Establish best practices for training, development and maintenance of data models, including A/B testing and communication of results to stakeholders.
• Own deployment of trained models into production in collaboration with Data or ML Engineers, ensuring reliable, observable deployment into Snowflake using DBT, integrating with existing pipelines and platform infrastructure, and maintaining version control via Git.
• Mine user data to identify opportunities for personalization improvements, defining and tracking KPIs related to personalization effectiveness.
• Develop and maintain data models in Snowflake to support analytical and reporting needs, providing insights to business stakeholders across various departments.
• Use Python to create ML models and structure the resulting data into a consumable flow.
• Develop user‑to‑user mapping capabilities to enhance personalization.
• Utilize search technologies (e.g., Algolia, AWS Open Search) to enhance product discovery and personalization.
• Analyze message content to detect potentially fraudulent activities, such as identifying keywords or phrases associated with scams or off‑platform transaction requests.
• Collaborate with product managers, engineers, designers, and business stakeholders to understand their data needs and provide data‑driven solutions.

• Graduate degree in data science, analytics, mathematics, machine learning, computer science, or related field a plus.
• Demonstrated track record of applying analytical skills in a product or business setting may substitute for formal advanced education.
• 8+ years of relevant work experience in a data or quantitative role, with demonstrated success in a startup, high‑growth or fast‑paced organization.
• Experience in marketplace, e‑commerce, or fashion/retail domains preferred.
• Experience with web + app product environment preferred.
• Experience with marketing analytics a bonus.
• Demonstrated success in non‑technical, cross‑functional partner communication.
• Ability to tell a story with data, explaining complex concepts or results to audiences ranging from C‑suite to IC levels.
• History of mentoring or developing teammates.
• Ongoing learning (e.g., certifications,…