# Software Engineer New Grad, Machine Learning Platform - Quora (Remote) **Company:** [Quora](https://scaleengineer.com/companies/quora) Join Quora's Machine Learning Platform team as a Software Engineer New Grad to design and optimize distributed systems serving 100+ production ML models at massive scale. You'll work across Python, Go, C++, PyTorch, Kubernetes, and AWS to build GPU serving infrastructure, feature stores, and ML developer tooling while learning from senior and staff engineers with dedicated mentorship and guidance toward shipping production impact within your first weeks. **Role:** ML Engineer **Seniority:** Junior **Locations:** Remote - Multiple Locations **Remote:** yes **Salary:** 97600–139000 USD [Apply](https://jobs.ashbyhq.com/quora/cf34f80e-fe5c-454d-bc9a-4c59993ffda0) Canonical: https://scaleengineer.com/jobs/quora/software-engineer-new-grad-machine-learning-platform-quora-remote --- ## Responsibilities - ML Infrastructure Development: Build and maintain core infrastructure powering Quora's ML platform, ensuring high availability, scalability, and reliability across distributed systems serving hundreds of millions of monthly users globally - Distributed Systems Optimization: Design and improve distributed systems for production ML model serving, focusing on architecture decisions that support deployment of Large Recommendation Models and Large Language Models at enterprise scale - GPU Model Serving: Optimize GPU model serving systems for latency reduction, throughput maximization, and cost efficiency while supporting increasingly capable and larger models through performance tuning and resource allocation strategies - Platform Modernization: Contribute to strategic platform initiatives including PyTorch-first standardization and ML ecosystem modernization to establish consistent, efficient development patterns across the organization - ML Developer Productivity: Build tooling and developer experience improvements enabling ML engineers to develop, test, and deploy models more efficiently, reducing time-to-production and enabling faster experimentation cycles - Feature Store Enhancement: Modernize feature store infrastructure to accelerate feature engineering workflows and reduce latency for ML engineers deploying new features into production environments - Production Support and On-Call: Participate in team on-call rotation, resolving production issues while expanding platform knowledge, building ownership, and developing incident response expertise on ML infrastructure systems ## Requirements ### education - {"name":"Bachelor's, Master's, or Ph.D. in Technical Field","description":"B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field required"} - {"name":"Expected Graduation 2025-2026","description":"Candidate should be graduating in 2025 or 2026, or have recently completed degree requirements"} ### technical - {"name":"Programming Language Proficiency","description":"Demonstrated knowledge of Python, Go, or C++ with ability to learn additional languages quickly for infrastructure development and systems optimization"} - {"name":"Distributed Systems Understanding","description":"Genuine interest in large-scale distributed systems architecture, including concurrency patterns, service communication, and infrastructure design at enterprise scale"} - {"name":"ML Infrastructure Fundamentals","description":"Foundational understanding of machine learning infrastructure concepts, though no previous production ML platform experience required as mentorship and guidance provided"} - {"name":"Systems-Level Performance","description":"Interest in low-level performance work including profiling, benchmarking, optimization, and GPU utilization maximization"} ### experience - {"name":"Software Engineering Background","description":"Previous software engineering experience preferred through internships, full-time work experience, significant open-source contributions, or competitive programming achievements"} - {"name":"ML Framework Experience","description":"Coursework or hands-on project experience with ML frameworks such as PyTorch or TensorFlow preferred but not required"} - {"name":"Cloud and Container Technologies","description":"Exposure to containerization (Docker), orchestration (Kubernetes), or cloud platforms (AWS, GCP, Azure) preferred"} ## Skills ### required - {"name":"Core Programming Languages","description":"Proficiency in Python, Go, or C++, with demonstrated ability to quickly acquire additional language skills as needed for infrastructure projects"} - {"name":"Systems Thinking","description":"Ability to reason about complex distributed systems, tradeoffs between reliability, performance, and scalability, and architectural design decisions"} - {"name":"Problem Solving","description":"Strong analytical and debugging skills with ability to approach infrastructure challenges methodically and derive solutions from first principles"} - {"name":"Collaborative Development","description":"Comfortable working in team environments, receiving mentorship, writing clear code for code review, and contributing to shared infrastructure systems"} - {"name":"Learning Agility","description":"Growth mindset with passion for continuous learning, expanding technical capabilities, and staying current with ML infrastructure advancements"} ### preferred - {"name":"PyTorch Expertise","description":"Hands-on experience building or deploying models with PyTorch framework for deep learning applications"} - {"name":"Kubernetes/Container Orchestration","description":"Practical experience with Kubernetes, EKS, Docker, or container-based infrastructure management in production environments"} - {"name":"AWS Cloud Services","description":"Familiarity with AWS services including EC2, container services, GPU instances, and cloud architecture patterns"} - {"name":"Performance Optimization","description":"Track record of performance-critical work including profiling tools, benchmarking methodologies, optimization techniques, or systems-level tuning"} - {"name":"Distributed Systems Experience","description":"Prior exposure to distributed computing frameworks, microservices architecture, or large-scale system design projects"} - {"name":"Quora Mission Alignment","description":"Demonstrated passion for Quora's mission to grow world's collective intelligence and interest in knowledge-sharing platforms"} ## Tech stack ### tools - {"name":"Kubernetes/EKS","description":"Container orchestration platform for managing distributed ML model serving infrastructure at scale"} - {"name":"NVIDIA Triton","description":"Model serving inference server for optimizing GPU utilization and multi-model serving efficiency"} - {"name":"Ray","description":"Distributed computing framework for scaling machine learning workloads and cluster resource management"} - {"name":"Docker","description":"Containerization platform for packaging and deploying ML services consistently across environments"} - {"name":"AWS","description":"Cloud infrastructure platform including EC2, ECS, and GPU-accelerated compute resources"} ### others - {"name":"Distributed Systems Architecture","description":"Design and optimization of large-scale systems serving hundreds of millions of users monthly"} - {"name":"GPU Performance Optimization","description":"Latency, throughput, and cost optimization for GPU-accelerated model serving"} - {"name":"ML Model Serving","description":"Production deployment patterns for Large Language Models (LLMs) and Large Recommendation Models (LRMs)"} ### databases ### languages - {"name":"Python","description":"Primary programming language for ML infrastructure, data processing, and platform development"} - {"name":"Go","description":"Used for building scalable backend services and distributed systems components"} - {"name":"C++","description":"Low-level performance optimization for GPU serving and high-throughput inference systems"} ### frameworks - {"name":"PyTorch","description":"Deep learning framework for building and deploying neural network models in production"} - {"name":"TensorFlow","description":"Machine learning framework experience preferred for ML model development and deployment"} ## Benefits ### benefits - {"name":"Comprehensive Medical Coverage","description":"Medical, dental, and vision insurance coverage with country-specific variations and competitive plan options"} - {"name":"Equity Compensation","description":"Equity refresher program with flexible equity compensation options allowing portion of equity to be taken as cash in eligible countries"} - {"name":"Remote Work Support","description":"Remote work reimbursement to support home office setup and equipment needs for distributed workforce"} - {"name":"Paid Time Off","description":"Generous paid time off policy to ensure work-life balance and time for personal wellness and development"} - {"name":"Employee Assistance Programs","description":"Comprehensive employee assistance programs including mental health support, counseling, and employee wellness resources"} - {"name":"Mentorship and Learning","description":"Dedicated mentorship from senior and staff engineers with structured technical guidance and professional development support"} - {"name":"Remote-First Culture","description":"Fully remote-first company culture with flexible work arrangements across United States and Canada"} ## Compensation - **max:** 139000 - **min:** 97600 - **currency:** USD - **stockOptions:** true ## Interview process ### steps - {"name":"Application Review","description":"Initial assessment of resume, background, and relevant experience with AI-assisted sorting to identify strong technical foundation and alignment with ML infrastructure team needs"} - {"name":"Technical Screening","description":"Coding assessment or technical discussion evaluating core programming language proficiency, problem-solving approach, and ability to reason about systems-level challenges"} - {"name":"Systems Design Interview","description":"Discussion of distributed systems thinking, infrastructure design principles, and approach to solving large-scale technical problems with trade-off analysis"} - {"name":"Team Collaboration Interview","description":"Conversation with team members exploring learning style, collaboration preferences, growth mindset, and cultural fit with ML platform engineering team"} - {"name":"Background Verification","description":"Identity verification and comprehensive background check for final candidates prior to offer and onboarding completion"} ## Full description _\[_[_**Quora is a privately held, "remote-first" company**_](https://www.quora.com/q/quora/Remote-First-at-Quora)_**.** This position can be performed remotely from anywhere in Canada or the United States. Please visit_ [_careers.quora.com/eligible-countries_](http://careers.quora.com/eligible-countries) _for details regarding employment eligibility by country.\]_ ## **About Quora:** Quora’s mission is to grow the world's collective intelligence. To do so, we have two platforms: * [Quora](http://quora.com): 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](https://poe.com/): 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! 👇✨ \- \- \- \- \- ## **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, 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**](https://quora.com/coordination%5Fhours)**" (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 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: #LI-SS2 #LI-REMOTE