Software Engineer, Machine Learning Platform, New Grad - Quora

Quora · AnywhereRemotojunior

En corto

▸Desarrollas infraestructura de ML a gran escala para Quora, con foco en servidores GPU y sistemas distribuidos.
▸Trabajas en un equipo de alto rendimiento, aprendiendo de ingenieros senior y desplegando en producción desde el primer mes.
▸Destaca el enfoque en optimizar costos y rendimiento con tecnologías como NVIDIA Triton y AWS EKS.

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¿Qué piden?

✓Título universitario en Ciencias de la Computación, Ingeniería o área relacionada (recién graduado).
✓Conocimiento sólido en Python, Go o C++.
✓Experiencia práctica con sistemas distribuidos o infraestructura de software.
✓Capacidad para aprender rápidamente tecnologías nuevas como Triton o Ray.
✓Inglés fluido para comunicación técnica y colaboración global.
✓Disponibilidad para trabajar de forma remota desde Canadá o EE.UU.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonGoC++PyTorchKubernetesEKSNVIDIA TritonRayAWSGit

¿A quién escribirle en Quora?

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[ Quora is a privately held, "remote-first" company . This position can be performed remotely from anywhere in Canada or the United States. 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 over 300M 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-5.6-Sol, Claude-Opus-5, Claude-Fable-5, Claude-Sonnet-5, Kimi-K3, and thousands of others. 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 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! 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 (LRM) to Large Language Models (LLM) 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 th

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