Machine Learning(ML) engineers at Dropbox's develop high impact products that advances the mission of Dropbox of designing a more enlightened way of working. Dropbox ML engineers deliver products using the full range of ML techniques including computer vision, natural language processing, recommendation systems and more. While some of our algorithms run on mobile devices, others require large clusters on our infrastructure.
Dropbox is looking for Staff Machine Learning Engineers with an academic or practical background in machine learning, ideally with experience in and familiar with state of the art in natural language understanding, information retrieval and recommendation systems.
As a Staff ML Engineer, you set direction for an area of ML and also execute on it. You deliver on multi-year and multi-team goals by collaborating with relevant product and infrastructure engineering teams.
Responsibilities
You will work within a machine learning team to design, code, train, test, deploy and iterate on large scale machine learning systems
You will build delightful products and experiences, while working alongside an excellent, multi-functional team across Engineering, Product and Design
You will help craft the direction of machine learning and artificial intelligence at Dropbox
Requirements
MS or PhD in Computer Science or related technical field involving Machine Learning, or equivalent technical experience
4+ years of experience building production machine learning systems
6+ years of experience building production engineering systerms
Strong analytical and problem-solving skills
Proven software engineering skills across multiple languages including but not limited to Python
Experience with machine learning software packages(e.g., scikit-learn, TensorFlow, Keras, PyTorch)
Desired Qualifications:
PhD in Computer Science or related field with research in machine learning
Experience with one or more of the following: natural language processing, recommendation systems, machine learning for information retrieval, machine learning on mobile devices
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