Machine Learning Engineer - Recommendation System, Quora (Remote)

[Quora is a "remote-first" company. This position can be performed remotely from multiple countries around the world. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country.]

About Quora:

Quora’s mission is to grow and share the world’s knowledge. To do so, we have two knowledge sharing products:

• Quora: a global knowledge sharing platform with over 400M monthly unique visitors, bringing people together to share insights on various topics and providing a unique platform to learn and connect with others.

• Poe: a platform providing millions of global users with one place to chat, explore and build with a wide variety of AI language models (bots), including GPT-4, Claude 3, Gemini Pro, DALL-E 3 and more. As AI capabilities rapidly advance, Poe provides a single platform to instantly integrate and utilize these new models.

Behind these products are passionate, collaborative, and high-performing global teams. We have a culture rooted in transparency, idea-sharing, and experimentation that allows us to celebrate success and grow together through meaningful work. Join us on this journey to create a positive impact and make a significant change in the world.

About the Team and Role:

Our Recommender System team works on challenging problems every day. Our Machine Learning Engineers are tasked with optimizing the content distribution system at Quora. The team covers the entire Machine Learning lifecycle from end-to-end, including feed generation, notifications, digest emails, and requests to answer. 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. Since we first launched our content distribution platform, we've grown to support millions of users who are engaging with over 400 million+ monthly unique visitors on Quora. Our journey is just beginning as we continue to build new products from the ground up and tackle exciting challenges at scale. Today, we're working on a broad range of areas to grow our content product to drive more value to our users. As a member of the Recommender System team, you will have a chance to work with a diverse and talented global team of engineers and collaborate closely with cross-functional partners to help users get the right content at the right time and build a stronger connection between Quora users and its knowledge base.

Responsibilities:

• 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 ad recommendation systems 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

• Improve the design of recommender model

• Work with product and business teams on new innovative features for recommender systems to optimize user and advertiser performance

• Identify new opportunities to apply machine learning to different parts of the Ads product to drive value for our users and advertisers

Minimum Requirements:

• 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 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

Preferred Requirements:

• Experience with leading large-scale multi-engineer projects

• Flexible and positive team player with outstanding interpersonal skills

• Passion for Quora's mission and goals

At Quora, we value diversity and inclusivity and welcome individuals from all backgrounds, including marginalized or underrepresented groups in tech, to apply for our job openings. We encourage all candidates who share a passion for growing the world’s knowledge, even those who may not strictly meet all the preferred requirements, to apply, as we know that a diverse range of perspectives can have a significant impact on our products and our culture.

Additional Information:

We are accepting applications on an ongoing basis.

Quora offers a wide range of benefits including medical/dental/vision coverage, equity refreshers, remote work reimbursement, paid time off, employee assistance programs, and more. Benefits are country-specific and may vary. For more information on benefits, visit this link: https://www.careers.quora.com/benefits

There are many factors that will determine the starting pay, including but not limited to experience, location, education, and business needs.

• US candidates only: For US based applicants, the salary range is $170,472 - $252,439 USD + equity + benefits.

• Canada candidates only: For Canada based applicants, the salary range is $166,632 - $264,379 CAD + equity + benefits.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Job Applicant Privacy Notice: https://www.careers.quora.com/applicant-privacy-notice

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