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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 advertisersMinimum 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 fieldPreferred Requirements:• Experience with leading large-scale multi-engineer projects• Flexible and positive team player with outstanding interpersonal skills• Passion for Quora's mission and goalsAt 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/benefitsThere 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#LI-REMOTE
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What You Should Know About Machine Learning Engineer - Recommendation System, Quora (Remote), Quora, Inc.

At Quora, we’re on a mission to grow and share the world's knowledge, and we need a talented Machine Learning Engineer for our Recommendation System team to help us achieve that. This remote position gives you the flexibility to work from anywhere in Texas or beyond, as long as you're willing to collaborate during our coordination hours. You'll be diving into the heart of our content distribution system, tackling challenges that not only enhance our machine learning solutions but also optimize the way our millions of users engage with content. In this role, you’ll take ownership of the entire machine learning lifecycle—think data pipelines, feature engineering, candidate extraction, and model training—all while refining our recommender model design. As part of our dynamic team, you’ll work closely with fellow engineers and cross-functional partners to innovate and implement features that drive user engagement and advertiser performance. The environment at Quora fosters continuous learning, experimentation, and a culture of transparency, which can be incredibly rewarding. If you are passionate about applying machine learning at scale and want to make a real impact, then this position might just be the right fit for you. Join us on this incredible journey, where your ideas matter, and together, we can build something extraordinary.

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Recommendation System, Quora (Remote) Role at Quora, Inc.
What are the main responsibilities of a Machine Learning Engineer at Quora?

As a Machine Learning Engineer specializing in recommendation systems at Quora, you will improve existing machine learning systems, take full ownership of the lifecycle from data pipelines to model training, and collaborate with engineers to build scalable systems. Your contributions will directly impact how users engage with our vast knowledge-sharing platform.

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What qualifications are needed for the Machine Learning Engineer position at Quora?

To qualify for the Machine Learning Engineer position at Quora, you should have 3+ years of professional software development experience in machine learning, knowledge of mathematical foundations of ML algorithms, and proficiency in Python, C++, or Golang. A degree in Computer Science or a related field is also a requirement.

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How does Quora support the growth of its Machine Learning Engineers?

Quora actively encourages the growth of its Machine Learning Engineers by fostering a culture of experimentation and learning. You'll have the autonomy to explore new ideas and work on various challenging problems while receiving support from collaborative teams. Regular feedback and real-time product iterations also help enhance your skills.

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What opportunities does the Machine Learning Engineer role offer at Quora?

The Machine Learning Engineer role at Quora offers opportunities to work on state-of-the-art machine learning algorithms, enhance user engagement through recommendation systems, and collaborate across teams. You'll also have the chance to implement innovative features that can significantly influence our platform's growth.

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Is the Machine Learning Engineer position at Quora remote, and what are the working hours?

Yes, the Machine Learning Engineer position at Quora is remote and can be performed from various locations. However, you will need to be available for meetings during coordination hours, which are Monday through Friday, 9 AM to 3 PM Pacific Time.

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Common Interview Questions for Machine Learning Engineer - Recommendation System, Quora (Remote)
Can you explain your experience with machine learning algorithms?

When answering this question, be specific about the algorithms you’ve worked with, any successful projects you led or were part of, and how you applied these algorithms in real-world scenarios. Highlight your understanding of concepts like overfitting, feature engineering, and model evaluation.

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Describe a challenging project you worked on involving a recommendation system.

Prepare to discuss the problem you faced, your approach to solving it, and the technology stack you used. Emphasize your role and contributions, any collaboration with team members, and the outcome of the project, focusing on the impact on user engagement or performance.

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How do you ensure the efficiency and scalability of your machine learning models?

Discuss your approach to building efficient algorithms, your experience with data handling (like batch processing), and your understanding of distributed systems. Explain any specific tools or techniques you've used to scale machine learning models effectively.

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What is your process for feature selection in a recommendation system?

Outline the methodologies you use for feature selection, such as correlation analysis, feature importance ranking, and others. Mention the importance of domain knowledge in selecting relevant features that drive model performance, and how you validate these features through experimentation.

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How do you handle data quality issues in your projects?

Explain your strategies for identifying and resolving data quality issues, such as incomplete records or noise in data. Include any tools you use and your approach to maintaining data integrity, like preprocessing steps or anomaly detection techniques.

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Can you walk us through your experience with deploying machine learning models?

Be ready to describe the deployment process you've followed, the challenges faced, and how you ensured the model remained performant in a production environment. Discuss any tools or frameworks you used for deployment and monitoring of models post-launch.

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What metrics do you consider crucial when evaluating the success of a recommendation system?

Highlight metrics such as click-through rate, conversion rate, precision, recall, or F1 score depending on the context of the recommendation system. Discuss how these metrics influence adjustments and enhancements to the system.

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How do you approach collaboration with cross-functional teams?

Discuss your experience working with product managers, UX designers, and other stakeholders. Highlight the importance of communication, setting clear goals, and understanding the business side to align your machine learning solutions with company objectives.

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What recent advancements in machine learning excite you the most?

Share your insights on new methodologies, tools, or breakthroughs in machine learning that inspire you. Relate these advancements to how they could potentially enhance Quora’s recommendation systems or user engagement initiatives.

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Why do you want to work for Quora as a Machine Learning Engineer?

Articulate your alignment with Quora's mission of sharing knowledge and bridging connections. Reflect on the company culture you've researched, the exciting challenges presented in the role, and how your skills and passion can contribute to the team's success.

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Quora is a world renowned question-and-answer platfrom and knowledge market headquartered in Mountain View, California. Since its foundation in 2010, Quora's website has been visited by 300 million users a month.

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CULTURE VALUES
Mission Driven
Passion for Exploration
Transparent & Candid
Growth & Learning
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Full-time, remote
DATE POSTED
December 6, 2024

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