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. In this role, you will: Act as a technical lead within the data team to advance our recommendation & search algorithms. You will focus on improving the relevance & quality of inventory impressions that are 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 in the market.) Form a high‑level perspective on objectives across departments in the organization and how advanced data methods might solve complex business problems. Be able to autonomously and proactively identify business problems that could benefit from data solutions, whether it be application of existing models or the need for the development of new model(s), and take ideas through all phases, from proposal to alignment to execution. Establish best practices for training, development and maintenance of data models. This includes using A/B testing and communicating results to stakeholders. Own the deployment of trained models into production in collaboration with Data or ML Engineers. You will be responsible for ensuring reliable, observable deployment into Snowflake using DBT, integrating with existing data pipelines and platform infrastructure, and maintaining version control of code and configurations via Git. Mine user data to identify opportunities for personalization improvements. This includes 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 (i.e. Algolia, AWS OpenSearch) to enhance product discovery and personalization. Analyze message content to detect potentially fraudulent activities, such as identifying keywords or phrases associated with scams, requests for off‑platform transactions, or attempts to phish for personal information. Collaborate with product managers, engineers, designers, and business stakeholders to understand their data needs and provide data‑driven solutions. We are looking for: 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, demonstrated success in a startup, high‑growth or faced‑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 nontechnical, 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. relevant certifications; open‑source contributions; personal projects; etc.) is a plus and shows initiative. Specific tools are less a requirement in this role than an ability to communicate with stakeholders, understand complex, industry‑specific problems, maintain a high, self‑motivated velocity and bias for action, and have a desire to contribute to problems big and small. That being said, we expect candidates to have Expert level grasps on SQL, Python and complex mathematical concepts related to recommendation and personalization engines, and bring an ability to effectively use coding agents to build and iterate. Our Data stack additionally contains Looker, Amplitude, DBT, Fivetran and AWS Lambdas, experience in these areas is a plus. Math and Statistics Proven expertise in advanced statistical modeling, causal inference, experiment/test design, and working knowledge of machine learning algorithms. Data Science and Engineering Expert level proficiency in Python for data manipulation, statistical analysis, and model development. Practical experience with vector databases and embeddings for tasks like user‑to‑user or user‑to‑item mapping, semantic search, or item similarity preferred. Experience with Snowflake for SQL and data‑warehousing preferred. Experience with DBT for building modular, version‑controlled data transformations preferred. Experience with Git for collaborative code development and review preferred. Machine Learning and AI Experience in designing, developing, deploying and optimizing Personalization and Recommendation products at scale. Experience building models to assess item/listing quality (as defined by likelihood of sales), classify listings, and use NLP on unstructured text. Experience modeling time‑series forecasts for market trends, seasonality, demand prediction and other relevant KPIs. Hiring Range Tier 1 (Includes states such as California, New York (including New York City), Washington, Illinois and other higher‑cost markets) – $128,000 - $160,000 USD Tier 2 (Includes mid‑cost markets across the U.S.) – $115,200 - $144,000 USD Tier 3 (All other U.S. locations) – $108,800 - $136,000 USD The hiring range for this position is below, plus benefits (401K, paid time off, dental, medical, vision, disability, life insurance options). To determine starting pay within the hiring range, we carefully consider a variety of factors, including primary work location, role/level, a candidate’s skills, experience, market demands, and internal parity. You may reach out to a recruiter for additional information. 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