Machine Learning Engineer, Quora

• Ability to be available for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
• 3+ years of professional software development experience in machine learning
• Previous experience working in Adtech, developing ad targeting/retrieval/recommender systems
• Previous experience building large scale ranking/recommendation systems
• Good understanding of mathematical foundations of machine learning algorithms
• Highly proficient coding ability writing Python, C++ or Golang code
• BS, MS or PhD in Computer Science, Engineering or a related technical field

Desirable

• Our Machine Learning Engineers are tasked with optimizing the advertising product at Quora
• The team covers the entire Machine Learning Ads lifecycle from end-to-end, including ads targeting, ranking and auction dynamics, and quality measurement
• Engrained in our culture is the desire to constantly learn and improve, and our engineers are encouraged to think big and experiment with new ideas
• Using continuous deployment, we quickly see our changes in the product and make fast iterations
• Our engineers focus on creating polished products and writing high-quality code by designing APIs and abstractions that are extensible and maintainable
• As a remote-first company, our engineers have a high degree of flexibility and autonomy
• Everyone on the engineering team has a huge impact on our product and our company
• Improve our existing machine learning systems using your core coding skills and ML knowledge
• Take end to end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model training, as well as integration into our production systems
• Apply state-of-the-art machine learning algorithms at scale and serving for next-generation targeting and bidding products that directly impact the company’s top line metrics
• Collaborate with ML platform and product engineers to build scalable and efficient machine learning systems in the production environment
• Work with product and business teams on new innovative features for ad targeting and bidding to optimize advertisement performance
• Identify new opportunities to apply machine learning to different parts of the Ads product to drive value for our users and advertisers