Machine Learning Engineering Manager
Posted on 3/31/2023
INACTIVE
Smart workspace & storage solutions
Company Overview
Dropbox’s mission is to design a more enlightened way of working with their custom-built storage infrastructure designed for a variety of needs. The company is committed to keeping life organized and keep work moving with their secure file sharing, collaboration, and storage solutions.
Locations
Remote • United States
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Java
Keras
C/C++
Pytorch
Tensorflow
Natural Language Processing (NLP)
Python
CategoriesNew
AI & Machine Learning
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
- 3+ years of people management experience with an ML engineering team
- ML domain knowledge to set and execute technical strategy (in partnership with the Tech lead)
- 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, Java or C/C++
- Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, HuggingFace etc.)
- Relevant experience can range from working on a wide-variety of optimization, and classification problems, e.g. recommender systems, natural language processing, 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 effectively
Responsibilities
- 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
- Understand the ML stack at Dropbox, and build systems that help Dropbox personalize their users' experience
- Collaborating with product and design teams to understand the requirements and constraints of the project and to ensure the models developed meet those requirements
- Mentoring and guiding senior and junior engineers in the development of their skills and knowledge
- Providing technical leadership and guidance to the team in the development, implementation, iteration, and deployment of machine learning models
- Set direction for the team, anticipate strategic and scaling-related challenges via thoughtful long-term planning
- Collaborating with other engineering teams to ensure that models are properly integrated into the larger product and shipped to production