Machine Learning Engineering Manager - Location Flexible at Dropbox (San Francisco, CA or Remote)
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Role Description
The Machine Learning - Go to Market team builds & applies ML models to drive Dropbox Wide Growth initiatives, leveraging a high fidelity understanding of users, content, and context, and support go-to-market strategy. This software engineering team sits within ‘Growth and Data’ organization, which seeks to leverage data to help make better business decisions. The team partners with Growth and Product teams to develop models, systems and features that leverage the massive scale of Dropbox’s user base to understand and predict user behavior to optimize the experience at all stages of the user journey at Dropbox. The team’s work on revenue impact has been often highlighted in Dropbox quarterly earning calls.
We're looking for a Machine Learning, Engineering Manager, who will build and lead a team of ML engineers to partner with growth, revenue, and product teams to answer key questions about how to grow revenue & scale the business via enabling better understanding of users through segmentation efforts. You’ll work alongside data science, product, engineering, design, marketing and research executives to advise on the company’s most impactful quantitative projects and achieve its most strategic business goal. You will work on process and infrastructural improvements to improve scalability and turnaround times. Our team culture rewards a bias for action, engineering partnership in defining the product, and discipline in how we develop. You’ll thrive in our team if you love chasing impact, working through ambiguity, and developing a culture of innovation.
To do this, you’ll align your teams’ roadmap to business objectives, and communicate insights & impact broadly across multiple leadership teams. You’ll also have the unique opportunity to influence growth teams through crafting strategies like personalization, upsell & cross sells. This role is highly strategic and uniquely is positioned to drive multiplier impacts for Dropbox as we continue to innovate and expand our core products. At the same time, you will get to lead an amazing team of talented ML engineers to shape the future of our business.
Responsibilities
- Grow and develop an incredible team of hardworking and motivated ML engineers with high expectations around individual ownership and impact
- Understand the ML stack at Dropbox, and build systems that help Dropbox personalize their users’ experience
- Lead the team to design, build, evaluate, deploy and iterate on large scale Machine Learning systems
- Set direction for the team, anticipate strategic and scaling-related challenges via thoughtful long-term planning
- Help with crafting technical & product strategy and work with cross functional teams (product, data science, growth, infra, etc.) to bring your models & features to life
- Ensure your team delivers extraordinary output, and continuously seeks ways to make an outsized impact
- Build production grade propensity models to drive personalization strategies, by utilizing advanced statistical modeling, machine learning, or data mining techniques
- Evaluate the performance of machine learning systems against business objectives
- Work with large scale data systems, and infrastructure
Requirements
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
- 5+ years of experience building Machine Learning or AI systems
- 2+ years of people management experience with an engineering team
- ML domain knowledge to set and execute technical strategy (in partnership with the TL)
- Strong industry experience working with large scale data
- Strong analytical and problem-solving skills
- Proven software engineering skills across multiple languages including but not limited to Python, Go, C/C++
- Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, etc.)
- Relevant experience can range from working on a wide-variety of optimization, and classification problems, e.g. segmentation, propensity modeling, text/sentiment classification, click-through rate prediction, collaborative filtering/recommendation, or spam detection.
- Excellent verbal and written communication skills
- Understand what matters most and prioritize ruthlessly
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