5 + years of experience building Machine Learning or AI systems.BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience.Collaborating with other engineering teams to ensure that models are properly integrated into the larger product and shipped to production.Set direction for the team, anticipate strategic and scaling-related challenges via thoughtful long-term planning.Providing technical leadership and guidance to the team in the development, implementation, iteration, and deployment of machine learning models.Mentoring and guiding senior and junior engineers in the development of their skills and knowledge.Collaborating with product and design teams to understand the requirements and constraints of the project and to ensure the models developed meet those requirements.Understand the ML stack at Dropbox, and build systems that help Dropbox personalize their users’ experience.Leading and managing a team of machine learning engineers in the development of cutting edge ML models, with a focus on recommender systems and language models.Your responsibilities will include, but not be limited to: At the same time, you will get to lead an amazing team of talented ML engineers to shape the future of our business. This role is highly strategic and uniquely positioned to drive big impact for Dropbox as we continue to innovate and expand our core products, and usher in the era of generative AI.
You will work closely with product, design, data science, and infrastructure teams to understand the requirements and constraints of the project, and you will guide your team to develop and deliver high-quality models that meet those requirements. We're looking for a Machine Learning, Engineering Manager, who will lead a t his team of talented ML engineers to partner with our Core product teams in bringing relevant and delightful ML first product experience to our customers. We leverage state-of-the-art algorithms and techniques such as collaborative filtering, natural language understanding, and sentiment analysis to deliver personalized recommendations and content organization for our users. The Recommendations team is responsible for designing, building, and deploying machine learning models that power our recommender systems and other content extraction models.
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