Software Engineer, Machine Learning Platform, New Grad - Quora (Remote)
ML Engineer · Intern · Full Time · Remote
Opens Quora's application page
Role
What you'll do.
Join Quora's Machine Learning Platform team as a Software Engineer in an accelerated new graduate program working at the intersection of machine learning, distributed systems, and GPU serving performance. You'll build and maintain core infrastructure powering 100+ production ML models serving hundreds of millions of users monthly, learning from senior and staff engineers with mentorship and shipping to production within your first weeks. This role requires a passion for large-scale distributed systems and machine learning, with no prior ML infrastructure experience necessary.
Responsibilities
- Build and Maintain ML Platform Infrastructure: Design, develop, and maintain core infrastructure that powers Quora's machine learning platform, focusing on ensuring high availability, reliability, and scalability across distributed systems serving hundreds of millions of monthly users.
- Optimize GPU Model Serving: Work on GPU-accelerated model serving optimization, improving latency, throughput, and cost efficiency to support deployment of Large Recommendation Models (LRM) and Large Language Models (LLM) at scale using NVIDIA Triton and Ray.
- Enhance ML Developer Velocity: Build and improve platform tooling that enables ML engineers to develop, test, validate, and deploy models more efficiently, reducing friction in the model development lifecycle and accelerating time-to-production.
- Modernize Feature Store Infrastructure: Contribute to feature store modernization initiatives that allow ML engineers to iterate faster on feature engineering and get new features into production with reduced latency and improved reliability.
- Implement Distributed Systems Solutions: Build and improve distributed systems that serve production ML models, handling model routing, fallback logic, and ensuring fault tolerance across multi-region deployments on Kubernetes/EKS.
- Drive Platform Modernization: Participate in platform initiatives such as PyTorch-first standardization and ML ecosystem modernization, contributing to strategic technical decisions that benefit all ML engineers across the organization.
- Support Production Operations: Participate in team on-call rotation to resolve production incidents, gaining hands-on experience with operational challenges and building deep ownership of platform components as you grow your expertise.
Qualifications
What we look for.
Technical
Programming Languages
Proficiency or ability to quickly learn Python, Go, or C++ for building scalable backend systems and performance-critical infrastructure components.
Distributed Systems Fundamentals
Understanding of distributed system concepts including concurrency, consistency models, fault tolerance, and distributed communication patterns essential for ML infrastructure work.
Machine Learning Concepts
Foundational knowledge of machine learning principles, model training, inference, and deployment patterns; experience with ML frameworks is a strong plus.
Software Engineering Best Practices
Solid grasp of software design patterns, code quality, testing methodologies, debugging techniques, and version control systems for collaborative development environments.
Education
Undergraduate or Graduate Degree
Current pursuit of or expected completion of a B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a closely related technical field by 2025 or 2026.
Experience
Communication Availability
Availability for synchronous meetings and impromptu communication during Quora's coordination hours (Monday-Friday, 9:00 AM - 3:00 PM Pacific Time) to facilitate real-time collaboration with distributed team members.
Learning Mindset
Demonstrated passion for continuous learning, intellectual curiosity about large-scale systems, and commitment to improving both personal capabilities and supporting team growth.
Software Engineering Background (Preferred)
Prior software engineering experience through internships, full-time roles, significant open-source contributions, or competitive programming achievements demonstrating practical coding skills.
Skills
Required
Python Programming
Strong Python proficiency for developing ML platform services, data pipeline orchestration, and infrastructure automation; core language used across Quora's ML ecosystem.
Systems Thinking
Ability to understand complex interactions between components in distributed systems and reason about scalability, performance bottlenecks, and failure modes.
Problem-Solving
Demonstrated ability to break down complex technical challenges, propose scalable solutions, and iterate effectively with mentorship and team feedback.
Communication Skills
Ability to articulate technical ideas clearly, collaborate effectively with cross-functional teams, and document architectural decisions and implementations.
Preferred
Go or C++
Nice to haveExperience with Go for services development or C++ for performance-critical code; valuable for working across Quora's ML platform technology stack.
PyTorch or TensorFlow
Nice to haveHands-on experience with popular machine learning frameworks through coursework, projects, or internships; deep understanding of model training and inference workflows.
Kubernetes and Container Orchestration
Nice to haveExposure to Kubernetes, Docker containerization, or AWS EKS deployment; understanding of container networking, resource management, and scaling patterns.
AWS Cloud Services
Nice to haveFamiliarity with AWS infrastructure services including EC2, S3, networking components, and GPU instances; understanding of cloud cost optimization and resource management.
Performance Optimization
Nice to haveExperience with profiling tools, benchmarking methodologies, latency optimization, or throughput improvements; any low-level systems performance work is highly valuable.
NVIDIA Triton or ML Serving
Nice to haveExperience with NVIDIA Triton Inference Server, Ray Serve, KServe, or similar ML model serving platforms; understanding of batching, dynamic shaping, and multi-model serving patterns.
Quora Platform Knowledge
Nice to haveActive engagement with Quora's knowledge-sharing platform and understanding of the company's mission to grow collective intelligence; demonstrated passion for the product.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 97,600 – 139,000
Equity·Stock options
Benefits
Comprehensive Health Coverage
Medical, dental, and vision insurance plans with competitive coverage to ensure you and your family maintain excellent health and wellness throughout your career at Quora.
Equity Compensation
Equity refreshers providing ongoing ownership stake in Quora's growth and success; allows employees to share in the company's long-term value creation and financial success.
Remote Work Support
Remote work stipend reimbursement to establish and maintain a productive home office environment, recognizing that Quora is a remote-first company supporting distributed teams.
Paid Time Off
Generous paid time off and vacation policies enabling you to recharge, maintain work-life balance, and take time for personal priorities while remaining connected to the team.
Employee Assistance Programs
Comprehensive employee assistance programs providing resources for mental health, financial wellness, legal services, and personal development support for holistic employee wellbeing.
Mentorship and Professional Development
Dedicated mentor from senior and staff engineers on the ML Platform team; structured guidance and investment in your technical growth, learning, and career development.
Learning and Development Opportunities
Access to learning resources, technical conferences, training programs, and educational reimbursement to support continuous skill development and career advancement.
Inclusive and Diverse Culture
Commitment to diversity, equity, and inclusion with welcoming environment for individuals from all backgrounds, including those from underrepresented groups in technology.
Process
Interview steps.
- 01
Application Screening
Initial application review where recruiters evaluate your resume, academic background, demonstrated interest in machine learning and distributed systems, and software engineering experience. AI technology may assist in preliminary sorting, but all decisions are made by team members.
- 02
Phone Screen
Initial conversation with a recruiter or engineer to discuss your background, technical interests, motivation for the ML platform role, and Quora's work culture. Establishes fit and clarifies role expectations for both parties.
- 03
Technical Interview - Systems Design
Technical evaluation focusing on distributed systems thinking, problem-solving approach, and ability to reason about scalability and reliability. Expect discussions about system architecture, tradeoffs, and real-world ML infrastructure challenges.
- 04
Technical Interview - Coding
Coding assessment in Python, Go, or C++ evaluating algorithmic thinking, code quality, debugging ability, and communication. Focus on clarity of thinking and problem-solving methodology rather than memorized solutions.
- 05
Team Interview
Conversation with team members to assess collaboration style, learning orientation, communication skills, and cultural alignment. Opportunity to learn more about day-to-day work, mentorship approach, and team dynamics on the ML platform team.
- 06
Background Check and Verification
Comprehensive background check and identity verification process for all final candidates prior to offer and onboarding, ensuring security and compliance requirements are met.
Full posting
Original listing.
[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 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 Claude-Opus-4.7, Nano-Banana-2, GPT-Image-2, GPT-5.5, GPT-5.5-Pro, 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 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/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 (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 the team's on-call rotation, helping resolve production issues as you grow your knowledge and ownership of the platform
Minimum Requirements:
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
Preferred Requirements:
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.
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 pay, 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.
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://www.careers.quora.com/pages/quora-global-job-applicant-privacy-notice
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