# Senior Machine Learning Engineer, Ranking - Quora (Remote)
**Company:** [Quora](https://scaleengineer.com/companies/quora)
Senior Machine Learning Engineer specializing in ranking and recommendation systems at Quora, a remote-first knowledge-sharing platform. This role focuses on advancing second-phase ranking models for high-impact surfaces like feed and digest using state-of-the-art ML techniques. You will own end-to-end machine learning systems including data pipelines, feature engineering, model training, and production integration, requiring 3+ years of professional experience building recommendation systems with strong mathematical foundations and proven expertise in production ML systems.
**Role:** ML Engineer
**Seniority:** Senior
**Locations:** Remote - Multiple Locations
**Remote:** yes
**Salary:** 189507–274604 USD
[Apply](https://jobs.ashbyhq.com/quora/b9b477ad-4c43-49f0-b1fb-e7ee218686a7)
Canonical: https://scaleengineer.com/jobs/quora/senior-machine-learning-engineer-ranking-quora-remote-b9b477ad
---
## Responsibilities

- Advance Ranking Models: Improve existing machine learning ranking models for feed and digest systems using core coding skills and advanced ML knowledge to optimize content distribution across Quora's platform
- End-to-End ML System Ownership: Take complete ownership of machine learning systems spanning data pipelines, candidate extraction, feature engineering, model training, evaluation, and seamless integration into production environments
- Identify ML Opportunities: Proactively identify and evaluate new opportunities to apply machine learning techniques to different product areas within the Quora platform
- Cross-Functional Collaboration: Work collaboratively with other machine learning engineers to design, implement, and deploy algorithms and systems efficiently while maintaining code quality and best practices
- Technical Leadership: Develop methodologies and best practices that other engineers will build upon, establishing standards for ML development and experimentation within the distribution team

## Requirements

### education

- {"name":"Bachelor's Degree","description":"BS, MS, or PhD in Computer Science, Engineering, or a related technical field required"}

### technical

- {"name":"Machine Learning Fundamentals","description":"Strong understanding of mathematical foundations of machine learning algorithms including linear algebra, probability, statistics, and optimization theory"}
- {"name":"Recommendation Systems","description":"3+ years of professional experience designing, building, and deploying recommendation and ranking models in production environments"}
- {"name":"Production ML Systems","description":"Proven expertise building end-to-end machine learning systems including data pipeline construction, feature engineering at scale, model training, evaluation, and production deployment"}
- {"name":"Programming Proficiency","description":"Strong core programming skills with ability to write clean, efficient, maintainable code in languages such as Python or C++"}
- {"name":"Coordination Hours Availability","description":"Availability for meetings and impromptu communication during Quora's coordination hours (Monday-Friday, 9am-3pm Pacific Time)"}

### experience

- {"name":"Recommendation Model Experience","description":"3+ years of professional experience working specifically on recommendation or ranking models in production systems"}
- {"name":"Communication Skills","description":"Demonstrated good communication and interpersonal skills with ability to collaborate effectively across engineering teams and explain complex technical concepts clearly"}

## Skills

### required

- {"name":"Machine Learning Algorithms","description":"Deep understanding of classification, regression, ranking algorithms, loss functions, regularization techniques, and model evaluation metrics"}
- {"name":"Feature Engineering","description":"Expertise in feature extraction, selection, transformation, and engineering techniques for machine learning systems at scale"}
- {"name":"Python or C++","description":"Proficiency in writing production-quality code in Python or C++ for machine learning model development and system integration"}
- {"name":"Statistical Analysis","description":"Strong foundation in statistical methods, hypothesis testing, and A/B testing for validating model improvements in production"}
- {"name":"Data Pipelines","description":"Experience designing and implementing data pipelines for ingesting, processing, and transforming large-scale datasets for model training"}

### preferred

- {"name":"Advanced Python or C++","description":"5+ years of professional experience writing optimized Python or C++ code with deep knowledge of performance optimization and system design"}
- {"name":"Generative Recommendation Systems","description":"Hands-on experience building recommendation systems that leverage generative models and large language models for content ranking"}
- {"name":"Sequence Modeling and Transformers","description":"Proven experience with user behavior modeling, user sequence analysis, transformer architectures, and long sequence modeling techniques applied to ranking problems"}
- {"name":"Large-Scale Project Leadership","description":"Experience leading substantial multi-engineer projects involving complex technical challenges and cross-functional coordination"}
- {"name":"Team Collaboration","description":"Demonstrated flexibility, positive team dynamics, and outstanding interpersonal skills in collaborative engineering environments"}
- {"name":"Mission Alignment","description":"Passion for Quora's mission of growing the world's collective intelligence through knowledge sharing and connection"}

## Tech stack

### tools

- {"name":"Jupyter Notebooks","description":"Interactive development environment for model experimentation, data exploration, and iterative machine learning prototyping"}
- {"name":"Git and Version Control","description":"Version control systems for managing code, collaborating with team members, and tracking model development iterations"}
- {"name":"MLflow or Weights & Biases","description":"Experiment tracking and model management tools for logging, versioning, and comparing machine learning experiments"}
- {"name":"Docker","description":"Containerization technology for packaging models and ensuring consistent environments across development and production"}

### others

- {"name":"A/B Testing Frameworks","description":"Statistical testing infrastructure for validating ranking model improvements in production with proper experimental rigor"}
- {"name":"Recommendation System Architectures","description":"Understanding of two-stage ranking systems, candidate generation, feature stores, and real-time inference serving"}
- {"name":"User Behavior Analysis","description":"Techniques for analyzing user interaction patterns, engagement metrics, and behavioral signals in content ranking"}

### databases

- {"name":"SQL Databases","description":"Relational databases for storing structured training data and metadata used in recommendation systems"}
- {"name":"NoSQL Databases","description":"Document and key-value stores for handling unstructured data and user behavior information in production systems"}
- {"name":"Data Warehousing","description":"Large-scale data warehouse solutions for aggregating and querying historical user interaction data for feature engineering"}

### languages

- {"name":"Python","description":"Primary language for machine learning model development, data processing, and rapid prototyping in production systems"}
- {"name":"C++","description":"Used for high-performance components, system optimization, and computationally intensive machine learning operations"}

### frameworks

- {"name":"Scikit-learn","description":"Machine learning library for classical algorithms, model evaluation, and feature engineering tasks"}
- {"name":"PyTorch","description":"Deep learning framework for building neural networks, transformer models, and advanced machine learning architectures"}
- {"name":"TensorFlow","description":"Alternative deep learning framework for building scalable machine learning models and production inference systems"}
- {"name":"Apache Spark","description":"Distributed computing framework for large-scale data processing and model training at scale"}

## Benefits

### benefits

- {"name":"Medical, Dental, and Vision Coverage","description":"Comprehensive healthcare benefits including medical, dental, and vision insurance for employee and family members (country-specific availability)"}
- {"name":"Equity Refreshers","description":"Ongoing equity compensation and refreshers as part of long-term incentive structure, with flexible equity program allowing portion to be taken as cash in eligible countries"}
- {"name":"Remote Work Support","description":"Remote work reimbursement to support home office setup and equipment needs as part of Quora's remote-first working arrangement"}
- {"name":"Paid Time Off","description":"Comprehensive paid time off and vacation policies supporting work-life balance and employee wellness"}
- {"name":"Employee Assistance Programs","description":"Employee assistance programs providing mental health support, counseling services, and wellness resources"}
- {"name":"Flexible Equity Compensation","description":"In equity-eligible countries, flexible equity program allowing portion of equity compensation to be taken as additional cash compensation"}

## Compensation

- **max:** 274604
- **min:** 189507
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Application Review","description":"Initial screening of application materials including resume, cover letter, and background against role requirements; AI technology may assist in sorting applications but human review makes final decisions"}
- {"name":"Technical Screening","description":"Phone or video screening focusing on machine learning fundamentals, recommendation systems experience, and technical depth in ranking model development"}
- {"name":"Coding and ML Problem","description":"Technical interview assessing core programming skills in Python or C++, data structure knowledge, and machine learning problem-solving abilities"}
- {"name":"System Design Discussion","description":"Architectural interview covering end-to-end machine learning system design including data pipelines, feature engineering, model training, and production deployment"}
- {"name":"Behavioral and Team Fit","description":"Conversation exploring teamwork, communication skills, project leadership experience, and alignment with Quora's mission of growing collective intelligence"}
- {"name":"Executive Conversation","description":"Meeting with engineering leadership to discuss impact potential, career growth opportunities, and strategic vision for ranking systems at Quora"}
- {"name":"Background Verification","description":"Comprehensive background check and identity verification conducted for all final candidates prior to onboarding"}

## Full description
_\[_[_**Quora is a privately held, "remote-first" company**_](https://www.quora.com/q/quora/Remote-First-at-Quora)_**.** This position can be performed remotely from multiple countries around the world. Please visit_ [_careers.quora.com/eligible-countries_](http://careers.quora.com/eligible-countries) _for details regarding employment eligibility by country.\]_

## **About Quora:**

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

* [Quora](http://quora.com): 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](https://poe.com/): 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**](https://quora.com/coordination%5Fhours)**" (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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