Enabling teams to quickly test and iterate on their ML hypotheses via ML training capabilities, reliable GPU compute infrastructure and experimentation tools such as distributed deep learning libraries and Python notebooks
Integrating GPU compute environment with large scale data and inference pipelines
Collaborating with cross-functional teams to integrate machine learning models into our platform
Ensuring scalability and efficiency of machine learning systems
Work across the stack to solve problems independently
Mentoring junior engineers and contributing to the team's growth
Who You Are:
We're looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact. The ideal candidate will have:
Bachelor, Master, Post-graduate or PhD in computer science, computing engineering, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline; or equivalent work experience
5+ years of industry experience working with high traffic or large data production environments, distributed systems, backend infrastructure, recommender systems and/or deep learning applications
5+ years experience with ML problems and platform tools either through first-hand modeling or close collaboration with modeling engineers or data scientists
Working knowledge of Jupyter notebooks and Python, plus experience with a compiled language, such as Scala, Java or C++
Nice to haves
You stay up-to-date on Machine Learning and Deep Learning industry trends
You have low level understanding of compute systems such as distributed storage, NVIDIA drivers and CUDA toolkits
