Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Remote - Multiple Locations · United States · Canada · United States
Live, first seen by us 46 days agoThere 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 $97,600 - $139,000 USD + equity + benefits. Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $125,320 - $142,783 CAD + equity + benefits. For all other locations in Canada, the salary range is $116,965 - $133,264 CAD + equity + benefits.
US candidates only: For US based applicants, the salary range is $97,600 - $139,000 USD + equity + benefits. Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $125,320 - $142,783 CAD + equity + benefits. For all other locations in Canada, the salary range is $116,965 - $133,264 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.Read the full posting
Location type: RemoteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: EngineeringFrom the employer’s posting
Job title: Software Engineer New Grad, Machine Learning Platform - Quora (Remote)
Will you now or in the future require sponsorship for employment visa status?From the employer’s application form
Build and maintain the ML platform's serving infrastructure for high availability and scale
Optimize GPU model serving for latency, throughput, and cost
Build tooling that helps ML engineers develop, test, and deploy models faster
Modernize the feature store so new features reach production faster
Take part in the on-call rotation to resolve production issues
Help build and maintain the core infrastructure that powers Quora's ML platform, ensuring high availability, scalability, and performance
Work on GPU model serving, optimizing latency, throughput, and cost to support larger and more capable models
Improve ML developer velocity by building tooling that helps ML engineers develop, test, and deploy models more efficiently
Modernize our feature store so ML engineers can get new features into production faster
Participate in the team's on-call rotation, helping resolve production issues as you grow your knowledge and ownership of the platform
Knowledge of Python, Go, or C++, or the ability to learn them quickly
Experience with low-level performance work of any kind: profiling, benchmarking, optimization
- Availability for meetings and impromptu communication during Quora's "coordination hours" (Mon-Fri: 9am-3pm Pacific Time) - A 2025 or 2026 graduate with or pursuing a B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field - Genuine interest in large-scale distributed systems, infrastructure, and machine learning
Quora’s mission is to grow the world's collective intelligence.
In the employer’s words · Read in context · Company website ↗
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Day 46
Similar jobs usually come down between day 8 and day 75.
At Quora, jobs usually come down by about day 36.
From 5,234 similar postings that came down, and 6 of Quora’s. Coming down is not always a hire.
5 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
No previous ML infrastructure experience is required for this role. You'll be joining a team of senior and staff engineers, learning this stack from the people who built it, with a dedicated mentor and strong technical guidance — and you'll be shipping to production in your first few weeks. Stack: Python, Go, C++, PyTorch, Kubernetes/EKS, NVIDIA Triton, Ray, AWS 🚀 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/Migrating-from-x86-to-AWS-Graviton-A-Journey-in-Cost-Optimization-and-Performance
- Genuine interest in large-scale distributed systems, infrastructure, and machine learning - Knowledge of Python, Go, or C++, or the ability to learn them quickly - A passion for learning and always improving yourself and the team around you
Stack: Python, Go, C++, PyTorch, Kubernetes/EKS, NVIDIA Triton, Ray, AWS 🚀 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/Migrating-from-x86-to-AWS-Graviton-A-Journey-in-Cost-Optimization-and-Performance - https://aws.amazon.com/blogs/containers/quora-3x-faster-machine-learning-25-lower-costs-with-nvidia-triton-on-amazon-eks/
🚀 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/Migrating-from-x86-to-AWS-Graviton-A-Journey-in-Cost-Optimization-and-Performance - https://aws.amazon.com/blogs/containers/quora-3x-faster-machine-learning-25-lower-costs-with-nvidia-triton-on-amazon-eks/ - https://quoraengineering.quora.com/Building-a-Service-Mesh-in-a-Hybrid-Environment
- Coursework or hands-on experience with ML frameworks such as PyTorch or TensorFlow - Exposure to Kubernetes, Docker, or cloud technologies like AWS - Experience with low-level performance work of any kind: profiling, benchmarking, optimization
- Work on GPU model serving, optimizing latency, throughput, and cost to support larger and more capable models - Contribute to platform initiatives such as PyTorch-first standardization and ML ecosystem modernization - Improve ML developer velocity by building tooling that helps ML engineers develop, test, and deploy models more efficiently
- Previous software engineering experience via an internship, work experience, open-source contribution, or coding competition - Coursework or hands-on experience with ML frameworks such as PyTorch or TensorFlow - Exposure to Kubernetes, Docker, or cloud technologies like AWS
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New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
[Quora is a privately held, "remote-first" company. This position can be performed remotely from anywhere in Canada or the United States. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country.]
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.
Machine Learning is central to Quora's mission of growing the world's collective intelligence. 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, serving hundreds of millions of people using Quora every month.
Our team owns Quora's ML platform and ranking infrastructure across four areas: serving reliability, ML engineer enablement and developer velocity, business impact, and cost efficiency. We want to empower all ML engineers at Quora to be as impactful as they can be in solving different ML problems at scale.
As a Software Engineer (New Grad) on this team, you'll work at the intersection of machine learning, distributed systems, and GPU serving performance — and your work will have an enormous impact on Quora's long-term success.
No previous ML infrastructure experience is required for this role. You'll be joining a team of senior and staff engineers, learning this stack from the people who built it, with a dedicated mentor and strong technical guidance — and you'll be shipping to production in your first few weeks.
Stack: Python, Go, C++, PyTorch, Kubernetes/EKS, NVIDIA Triton, Ray, AWS
🚀 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/Migrating-from-x86-to-AWS-Graviton-A-Journey-in-Cost-Optimization-and-Performance
- 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
Help build and maintain the core infrastructure that powers Quora's ML platform, ensuring high availability, scalability, and performance
Build and improve the distributed systems that serve our ML models in production, from Large Recommendation Models (LRMs) to Large Language Models (LLMs)
Work on GPU model serving, optimizing latency, throughput, and cost to support larger and more capable models
Contribute to platform initiatives such as PyTorch-first standardization and ML ecosystem modernization
Improve ML developer velocity by building tooling that helps ML engineers develop, test, and deploy models more efficiently
Modernize our feature store so ML engineers can get new features into production faster
Participate in the team's on-call rotation, helping resolve production issues as you grow your knowledge and ownership of the platform
Availability for meetings and impromptu communication during Quora's "coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
A 2025 or 2026 graduate with or pursuing a B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field
Genuine interest in large-scale distributed systems, infrastructure, and machine learning
Knowledge of Python, Go, or C++, or the ability to learn them quickly
A passion for learning and always improving yourself and the team around you
Previous software engineering experience via an internship, work experience, open-source contribution, or coding competition
Coursework or hands-on experience with ML frameworks such as PyTorch or TensorFlow
Exposure to Kubernetes, Docker, or cloud technologies like AWS
Experience with low-level performance work of any kind: profiling, benchmarking, optimization
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.
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 $97,600 - $139,000 USD + equity + benefits.
Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $125,320 - $142,783 CAD + equity + benefits. For all other locations in Canada, the salary range is $116,965 - $133,264 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
#LI-SS2
#LI-REMOTE
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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 $97,600 - $139,000 USD + equity + benefits. Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $125,320 - $142,783 CAD + equity + benefits. For all other locations in Canada, the salary range is $116,965 - $133,264 CAD + equity + benefits.
US candidates only: For US based applicants, the salary range is $97,600 - $139,000 USD + equity + benefits. Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $125,320 - $142,783 CAD + equity + benefits. For all other locations in Canada, the salary range is $116,965 - $133,264 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.
United States
[Quora is a privately held, "remote-first" company. This position can be performed remotely from anywhere in Canada or the United States. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country.] About Quora:
- https://aws.amazon.com/blogs/containers/quora-3x-faster-machine-learning-25-lower-costs-with-nvidia-triton-on-amazon-eks/ - https://quoraengineering.quora.com/Building-a-Service-Mesh-in-a-Hybrid-Environment - https://quoraengineering.quora.com/Building-Embedding-Search-at-Quora
More relevant text appears in the full description.
No clear work-authorization passage found. Eligibility is unconfirmed.