Description review

Senior Machine Learning Engineer, Ads - Quora

Quora · Canada, Ireland, USA · back to the listing

HR standards

59/100

needs work

Title ↔ description

90/100

strong

Reads as

Machine Learning Engineer

100% confident

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

How others title the same work

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What the listing never says

  • 26 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

[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 Monetization team works on challenging problems every day. Our Machine Learning Engineers are tasked with optimizing the advertising product at Quora, and the team covers the entire Machine Learning Ads lifecycle from end-to-end, including ads targeting, ranking and auction dynamics, and quality measurement. Ingrained 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. As a remote-first company, our engineers have a high degree of flexibility and autonomy, and everyone on the engineering team has a huge impact on our product, revenue and company.

Since we first launched our advertising platform, we've grown to support thousands of advertisers who are reaching over 300 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. We are looking for an experienced Machine Learning Engineer to join the Ads ML team as an ads ranking specialist. You will improve CTR and CVR prediction, model calibration, user and ad representations, and user-sequence modeling, translating improvements in ranking quality into measurable advertiser value, revenue, and better user experiences. This is a small, close-knit team where you own problems end-to-end — research, data, modeling, deployment and maintenance — and where your work has a direct line to the company's top line.

Responsibilities:


Develop and improve ads ranking models, including prediction objectives, feature interactions, user-history modeling, and calibration


Take end to end ownership of machine learning systems - from data pipelines, feature engineering, training-data construction and model evaluation, model training, as well as integration into our production systems


Evaluate and apply advances in deep learning and recommendation modeling to improve ads ranking within production latency, reliability, and cost constraints


Collaborate with ML platform and product engineers to build scalable and efficient machine learning systems in the production environment


Partner with product, data science, and engineering teams to define ranking objectives, design A/B experiments, and measure improvements in advertiser performance, revenue, and user relevance


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

Minimum Requirements:


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


4+ years of professional software development experience in machine learning


Hands-on experience developing and deploying ads ranking models at scale, including CTR or CVR prediction and calibration, with demonstrated ownership of production improvements


Experience evaluating ranking models through offline analysis and online experiments, including investigating discrepancies between model metrics and business outcomes


Experience using AI-assisted development tools for coding, testing, debugging, or data analysis, with sound judgment in validating generated code and conclusions


Hands-on experience building and deploying deep learning models with PyTorch or TensorFlow


Good understanding of mathematical foundations of machine learning algorithms


Strong Python programming skills and experience writing maintainable production ML code. proficient coding ability writing Python


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

Preferred Requirements:


Experience with modern ranking architectures, such as feature interaction networks, attention-based user-sequence models, and multi-task learning


Understanding of how ranking predictions and calibration interact with bidding and auctions to affect ad delivery and advertiser outcomes


Experience with leading large-scale multi-engineer projects


Experience addressing ranking challenges such as sparse or delayed conversion labels, sampling and exposure bias, cold-start users, or training-serving inconsistencies


Experience with generative recommender systems


Effective communicator with strong leadership 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 pay, 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.

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://www.careers.quora.com/pages/quora-global-job-applicant-privacy-notice

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How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 8 points (from 67 before penalties). Reviewed 23 Sep 2026.