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Senior Machine Learning Engineer, Ranking – Quora

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Remote from
USA, Canada, Ireland
Salary
USD 189,507–274,604 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
8 Nov 2026
Listing views
50
Application actions
3
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AI Summary

The role, at a glance.

Quora is hiring a Senior Machine Learning Engineer to own and improve second-phase ranking models for high-impact recommendation surfaces, including feed, notifications, and digest emails. The role covers the full machine learning lifecycle: data pipelines, candidate extraction, feature engineering, model training, and production integration. Candidates need at least three years of recommendation or ranking-model experience, strong ML fundamentals, and the ability to collaborate during Pacific Time coordination hours. Preferred expertise includes Python or C++, generative recommendation, user-sequence or transformer modeling, and leadership of large engineering initiatives. This is a remote-first, full-time role available across multiple eligible countries.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, high-leverage ML role requiring ownership of production ranking systems and deep experience with recommendation modeling. Success demands both rigorous modeling expertise and the engineering ability to deploy, evaluate, and iterate at scale.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$232,056
US market range$180k–$290k
AI insightThe disclosed US base salary range is $189,507 to $274,604 USD yearly, with a midpoint of $232,055.50. For a US-based senior machine learning engineer specializing in ranking and recommendation systems, an estimated market base-salary range is $180,000 to $290,000 USD yearly; equity and benefits may add meaningful total compensation. Canada-specific salary ranges are separately disclosed in CAD and vary by location.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you evaluate an update to a second-stage ranking model for a content feed?

I would begin with offline evaluation using time-based validation and ranking metrics such as NDCG, MRR, recall, calibration, and slice-level performance. I would then run a guarded online experiment with primary engagement metrics, quality and retention guardrails, and segment analysis to ensure gains are durable and do not create harmful feedback loops.

Describe how you would design an end-to-end recommendation pipeline for Quora digest emails.

I would separate candidate generation, eligibility and policy filtering, feature enrichment, ranking, and post-ranking diversification. The design would include reliable batch or near-real-time feature pipelines, reproducible training data, monitoring for freshness and drift, and experiment instrumentation that ties email recommendations to downstream user outcomes.

What challenges arise when using transformer or long-sequence models for ranking?

The main challenges are sequence length, serving latency, training cost, sparse or biased behavioral signals, and leakage across time windows. I would use careful temporal data splits, compact architectures or distillation where needed, efficient retrieval-plus-reranking designs, and online monitoring to verify that offline improvements translate into product impact.

How would you address popularity bias in a recommendation model?

I would first quantify exposure concentration and performance across content, creator, and user cohorts. Potential approaches include propensity-aware learning, exploration strategies, diversity-aware reranking, calibrated objectives, and constraints that balance relevance with long-tail discovery; all changes should be evaluated against both engagement and ecosystem-health metrics.

Tell us about leading a multi-engineer machine learning project with ambiguous requirements.

I would align the team on the user problem, measurable success criteria, constraints, and a staged technical plan before dividing ownership. I would use written design documents, milestone-based experimentation, clear interface contracts, and regular risk reviews, while preserving enough flexibility to adapt as data and experiment results change the direction.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

[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.]

About Quora:

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.

Responsibilities:

  • 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

Minimum Requirements:

  • 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

Preferred Requirements:

  • 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.

Additional Information:

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

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