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Quora

Software Engineer New Grad, Machine Learning Platform - Quora

Remote (Canada, UK, USA) · Remote · US only Posted today

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Skills this job asks for

Machine learningA/B testingPythonC++PyTorchKubernetesAWSLLMs

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Where you can work from

United States only

Based on the listing's location: Remote (Canada, UK, USA).

The company's headquarters is not the same as where you may work from. Visa sponsorship only matters if you would relocate; see below.

Visa sponsorship, if you would relocate

Sponsorship verdict

No sponsorship evidence yet

No government record and no wording either way. Not a refusal: ask the recruiter.

  • No government sponsor record hereThis employer posted directly and does not match a government sponsor register.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What Quora paid sponsored hires in similar roles4 certified filings for “Software Engineer” (Software Developers) in CA: $188k–$201k, median $195k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What Quora paid sponsored hires in similar roles
4 certified filings for “Software Engineer” (Software Developers) in CA: $188k–$201k, median $195k. Most were filed at wage level II (100%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

Before you apply

Sponsorship unknown

No register record and no sponsorship wording in the posting. Worth asking the employer before investing significant time.

These come from the posting’s own wording. Read the original listing to confirm. See better-fit alternatives →

About the role

[ 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 : 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: 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://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 - https://quoraengineering.quora.com/Feature-Engineering-at-Quora-with-Alchemy Responsibilities: • 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,

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