Staff Machine Learning Engineer, Recommendation Systems - Quora (Remote)

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

About [Employer]:

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

• [Employer]: 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 [Employer] product.

About the Team and Role:

At [Employer], machine learning drives nearly every aspect of our product, and our lean, high-velocity engineering team tackles complex, high-impact challenges daily. We thrive in a remote-first culture built on absolute autonomy, continuous learning, and bold experimentation.

We are looking for a Staff Machine Learning Engineer to define the technical future of [Employer]’s recommendation ecosystem. In this role, you will own the technical direction for feed ranking and retrieval, lead our highest-impact modeling bets, and pioneer the integration of LLMs and generative models to redefine how we understand and rank content. Beyond the home feed, you will also scale recommendation surfaces like notifications and digest emails, while architecting the underlying tools and abstractions that empower our entire engineering organization.

This is a high-leverage, hands-on IC role with organization-wide scope. You will write production code, mentor rising talent, and actively shape how our team leverages AI-assisted development to accelerate our shipping velocity.

Responsibilities:

• Set the technical direction for [Employer]’s home feed recommendation systems
• Lead the highest-impact projects across the recommender stack, from candidate generation and retrieval to ranking, re-ranking, and multi-objective optimization
• Drive measurable improvements in user engagement and content quality through well-designed online experiments
• Take end-to-end ownership of ML systems, including data pipelines, candidate extraction, feature engineering, model training, and production integration
• Identify new opportunities to apply machine learning to different parts of the [Employer] product
• Partner with product, data science, and infrastructure teams to align on priorities and tradeoffs
• Mentor engineers and lead by example, raising the bar for code quality, modeling rigor and AI assisted dev velocity

Minimum Requirements:

• Availability for meetings and impromptu communication during [Employer]'s “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
• 6+ years of professional software development experience in machine learning
• 4+ years of professional experience building and optimizing Home Feed recommendation systems for social media and user-generated content platforms.
• Deep knowledge of the full recommender stack: candidate generation, retrieval, ranking, re-ranking, embeddings, and multi-objective optimization.
• Deep understanding of mathematical foundations of machine learning algorithms
• Experience leading large-scale ML projects end-to-end with measurable outcome
• Strong communication and interpersonal skills
• BS, MS, or PhD in Computer Science

Preferred Requirements:

• 6+ years of experience writing Python or C++ code
• Hands-on experience deploying and running generative models at scale
• Background in notifications, email/digest recommendations, or growth and lifecycle modeling
• Flexible and positive team player
• Publications at venues like RecSys, KDD, NeurIPS, or ICML.
• Passion for [Employer]'s mission and goals

At [Employer], 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. This role is a backfill for an existing vacancy.

[Employer] 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 $219,989 - $320,613 USD + equity + benefits.
• Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $282,469 - $329,337 CAD + equity + benefits. For all other locations in Canada, the salary range is $263,638 - $307,381 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.

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