Quora
Staff Software Engineer - Machine Learning Platform, Quora (Remote)
Job Summary
This role involves designing, developing, and maintaining the core infrastructure that supports Quora’s machine learning platform, with a focus on scalability, reliability, and performance. The candidate will build distributed systems for serving machine learning models and optimize infrastructure efficiency at large scale. Key responsibilities include collaborating with ML engineers, supporting infrastructure needs, and working with technologies like Kubernetes, TensorFlow, and PyTorch. The position requires extensive experience in ML systems, infrastructure, and production coding, with a capacity to address technical challenges across high-performance distributed environments.
Required Skills
Benefits
Job Description
[Quora is a privately held, "remote-first" company.This position can be performed remotely from multiple countries around the world. Please visitcareers.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.
This role will be working on our Quora product.
About the Team and Role:
Machine Learning plays an important role in helping Quora further its mission of growing and sharing the world's knowledge. We have 100+ Machine Learning models in production powering various product features. We use a variety of algorithms — everything from linear models to decision trees and deep neural networks. Our production models operate at a huge scale and help over a hundred million people using Quora every month.
We want to empower all ML engineers at Quora to be as impactful as they can be in solving different ML problems at scale. To that end, we are looking for engineers to help us build our company-wide ML development platform. In this role, you will be the part of a small team solving very interesting technical problems at the intersection of exciting domains like Machine Learning, Distributed Systems and High Performance Computing. Your work will have an enormous impact on Quora's long-term success. This role will focus primarily on ML infrastructure and Distributed System (80%), with some involvement in supporting model deployment (20%).
🚀 Excited to see our MLP team's amazing work in action? Check out some of the incredible projects they've completed below! 👇✨
https://quoraengineering.quora.com/Building-a-Service-Mesh-in-a-Hybrid-Environment
https://quoraengineering.quora.com/Building-Embedding-Search-at-Quora
https://quoraengineering.quora.com/Feature-Engineering-at-Quora-with-Alchemy
Responsibilities:
Design, develop, and maintain the core infrastructure that powers Quora's machine learning platform, ensuring high availability, scalability, and performance
Build scalable and reliable distributed systems for serving machine learning models
Optimize infrastructure performance across the ML platform, identifying and resolving bottlenecks to meet the demands of large-scale machine learning workloads
Collaborate with machine learning engineers to understand their infrastructure needs and provide solutions that enable them to build and deploy models efficiently
Contribute to the design and implementation of our next-generation machine learning infrastructure, focusing on scalability, reliability, and cost-effectiveness
Develop services on top of open source technologies like Kubernetes, Tensorflow, and PyTorch
Own business-critical infrastructure, help resolve production issues, and participate in the team-wide on-call rotation
Minimum Requirements:
Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
Experience with building and owning end-to-end machine learning or data science-related systems
Experience instrumenting ML workloads for performance monitoring/efficiency
Experience with high performance, large scaled distributed systems
5+ years of industry experience in Machine Learning, Infrastructure or related fields
5+ years of experience writing production code in Python, C++, or similar language
BS or MS in Computer Science, Engineering or a related technical field
Preferred Requirements:
Strong communication and inter-personal skills, experience working with ML teams is a plus
Experience working with Kubernetes, Docker, Terraform, or other forms of containerized infrastructure
Hands-on experience with AWS technologies like EC2, EBS, S3, EKS
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.
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/benefits
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 $155,656 - $267,615 USD + equity + benefits.
Canada candidates only: For Canada based applicants, the salary range is $194,934 - $287,848 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-SD1
#LI-REMOTE
Quora
Quora is a place where you can ask questions that matter to you and get answers from people who have been there and done that. Quora is where scientists, artists, entrepreneurs, mechanics, and homemakers take refuge from misinformation and incendiary arguments to share what they know.
See more jobsSafe Remote Job Search Tips
Verify Employer Thoroughly
Research the company's identity thoroughly before applying. Check for a professional website with contacts, active social media, and LinkedIn profiles. Verify details across platforms and look for reviews on Glassdoor or Trustpilot to confirm legitimacy.
Never Pay to Get a Job
Legitimate employers never require payment for applications, training, background checks, or equipment. Always reject upfront payment requests or demands for bank details, even if they claim it's for purchasing necessary work gear on your behalf.
Safeguard Your Personal Information
Protect sensitive data like SSN, bank details, or ID copies. Share this only after accepting a formal, written job offer. Ensure it's submitted via a secure company system or portal, never through insecure channels like standard email attachments.
Scrutinize Communication & Interviews
Watch for communication red flags: poor grammar, generic emails (@gmail), vague details, or undue pressure. Be highly suspicious of interviews held only via text or chat apps; legitimate companies typically use video or phone calls.
Beware of Unrealistic Offers
If an offer's salary or benefits seem unrealistically high for the work involved, be cautious. Research standard pay for similar roles. Offers that appear 'too good to be true' are often scams designed to lure you into providing information or payment.
Insist on a Formal Contract
Always secure and review a formal, written job offer or employment contract before starting work or sharing final personal details. Ensure it clearly defines your role, compensation, key terms, and conditions to avoid misunderstandings or scams.