Senior Machine Learning Engineer, Ranking - Quora (Remote)

• Improve existing machine learning ranking models using coding skills and ML knowledge
• Own end-to-end ML systems including data pipelines, candidate extraction, feature engineering, model training, and production integration
• Identify new opportunities to apply machine learning across Quora product
• Collaborate with other ML engineers to implement algorithms and systems efficiently

[Quora is a privately held, "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.]

Quora’s mission is to grow the world's collective intelligence. To do so, we have two platforms:

• Quora: a global knowledge sharing platform with millions of 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 cloud workspace where millions of users run multiple AI agents on shared context and tools. One subscription, every frontier model, and the collaboration layer that makes them work together.

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. This role will be working on our Quora product.

About the Team and Role: Our small engineering team works on challenging problems every day. We have a culture that's rooted in constantly learning and improving, and our engineers are encouraged to think big and experiment with new ideas. As a remote-first company, our engineers have a high degree of flexibility and autonomy. Everyone on the engineering team has a significant impact on our product and our company. We are looking for an experienced machine learning engineer to join our distribution team, working on recommendation systems such as feed, notifications, and Quora's famous digest emails. At Quora, we use machine learning in almost every part of the product and recommendation systems play an important role in connecting users with content. As a ranking modeling expert, you will own and advance the second-phase ranking model behind feed and digest, one of the highest-leverage surfaces at Quora, by applying the latest machine learning techniques. You will also play a key role in developing methodologies that our other developers will build on top of.

• Improve our existing machine learning ranking models using your core coding skills and ML knowledge

• Take end-to-end ownership of machine learning systems - data pipelines, candidate extraction, feature engineering, model training, as well as integration into our production systems

• Identify new opportunities to apply machine learning to different parts of the Quora product

• Work with other machine learning engineers to implement algorithms and systems efficiently

• Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)

• 3+ years of professional experience working on recommendation or ranking models

• Good understanding of mathematical foundations of machine learning algorithms

• Previous experience building end-to-end machine learning systems

• Good communication and interpersonal skills

• BS, MS, or PhD in Computer Science, Engineering, or a related technical field

• 5+ years of experience writing Python or C++ code

• Experience with generative recommendation systems

• Experience with user behavior or user sequence modeling, transformers, or long sequence modeling applied to ranking

• 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.

We are accepting applications on an ongoing basis. This role is a backfill for an existing vacancy. 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. There are many factors that will determine the starting compensation, including but not limited to experience, location, education, and business needs.

• US candidates only: For US based applicants, the salary range is $189,507 - $274,604 USD + equity + benefits.

• Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $243,330 - $282,076 CAD + equity + benefits. For all other locations in Canada, the salary range is $227,108 - $263,271 CAD + equity + benefits.

• In equity-eligible countries, we currently also offer a flexible equity program where a portion of equity compensation may be taken as cash.

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. AI technology may assist in sorting applications and recording interview notes, but all decisions are made by a member of our team. To ensure a secure hiring process, all final candidates will undergo identity verification and a comprehensive background check prior to onboarding. Job Applicant Privacy Notice: https://careers.quora.com/quora-global-job-applicant-privacy-notice

#LI-SS2 #LI-REMOTE

Sourced from the a16z Speedrun talent network — apply on Speedrun.