Quora

Machine Learning Engineer, New Grad - Quora (Remote)

Quora2 weeks ago
Location

Remote - Multiple Locations

Workplace

Remote

Type

Full Time

Salary

USD 107,360 – 152,900

Level

Junior

Role

ML Engineer

Posted

Jul 8, 2026

Full TimeRemoteJunior

The role

Summary

Join Quora as a Machine Learning Engineer (New Grad) to build and iterate on consumer-facing AI systems powered by large language models. Working within a small, high-impact engineering team on the Quora platform, you'll develop scalable generative AI features, run structured experiments to measure engagement and quality metrics, and collaborate across the AI engineering stack from prompt engineering to agentic workflows. This role offers hands-on model development experience with emphasis on productionization, reliability, and optimization of production-grade AI systems serving 300M+ monthly users.

What you'll do

Consumer-Facing AI Feature Development: Build and iterate on production-quality AI features powered by large language models (LLMs) and generative AI systems that directly impact Quora's core product experience. Collaborate closely with engineering partners to implement transformer-based architectures and prompt engineering strategies that enhance user engagement and content quality across the platform.
Cross-Functional AI Engineering Collaboration: Work with specialized engineers across the AI engineering stack including prompt engineering specialists, agentic workflow optimization engineers, and evaluation systems engineers. Participate in design discussions and code reviews to ensure robust, maintainable AI solutions that leverage best practices in the broader generative AI landscape.
Structured Experimentation and Measurement: Design and execute rigorous experiments including A/B tests, offline evaluations, and statistical analysis to quantify the impact of AI features on engagement metrics, content quality, trust scores, and user satisfaction. Document findings and communicate results to inform product iteration and prioritization decisions.
Product-Engineering Translation: Collaborate with product managers and cross-functional engineering teams to translate user needs, business requirements, and engagement goals into scalable, technically feasible AI-powered solutions. Participate in requirements gathering, feasibility assessments, and iterative refinement of feature specifications.
Model Reliability and Production Safety: Strengthen the reliability of production AI systems through comprehensive monitoring dashboards, safety guardrails, quality assurance analysis, and systematic failure mode investigation. Implement detection mechanisms for degradation, hallucinations, and bias to ensure trustworthy AI outputs at scale.
Performance Optimization and Scalability: Optimize the latency, inference cost, and computational scalability of production AI systems to support millions of concurrent users. Profile system performance, implement caching strategies, and explore techniques such as quantization and model distillation to enhance efficiency without sacrificing quality.

What we look for

Technical

Machine Learning FundamentalsStrong foundational understanding of core machine learning algorithms, statistical learning theory, and mathematical principles underlying supervised and unsupervised learning methods. Demonstrated knowledge of linear algebra, probability, and optimization techniques essential for algorithm design and model evaluation.
Large Language Models and TransformersHands-on experience with large language models (LLMs) and transformer architectures. Understanding of attention mechanisms, tokenization, fine-tuning approaches, and practical experience with LLM APIs and frameworks. Familiarity with prompt engineering techniques and retrieval-augmented generation (RAG) systems is highly valued.
Python or C++ ProgrammingProficiency in Python or C++, with the ability to write clean, maintainable, and well-tested code. Python is strongly preferred for machine learning work; demonstrated ability to learn either language quickly if not already fluent.
Generative AI Systems KnowledgeUnderstanding of generative AI systems architecture, including model serving infrastructure, inference optimization, and deployment patterns. Familiarity with frameworks like PyTorch, TensorFlow, or JAX, and experience with ML operations (MLOps) practices.
Written Communication ExcellenceStrong command of written English with demonstrated ability to evaluate and assess tone, accuracy, clarity, and nuance in written content. Critical for a knowledge-sharing platform; ability to document technical decisions, communicate results, and provide clear technical writing.

Education

Bachelor's, Master's, or Ph.D. in Computer ScienceB.S., M.S., or Ph.D. in Computer Science, Computer Engineering, Machine Learning, or closely related technical field. Expected graduation date of 2025 or 2026 for new graduate positions. Coursework should include machine learning, algorithms, data structures, and mathematics foundations.

Experience

Software Engineering Experience (Preferred)Previous exposure to professional software engineering practices through internships, work experience at technology companies, competitive programming (such as Codeforces or LeetCode), or open-source contributions. Practical experience with software development workflows, version control, and collaborative engineering is advantageous.
Production-Scale Agent Development (Preferred)Hands-on experience building and deploying AI agents in production environments at meaningful scale. Understanding of challenges in agentic systems such as reliability, cost management, and user experience implications. Experience with agentic workflows, tool integration, and prompt optimization for agent behavior.
Passion for Continuous LearningDemonstrated commitment to rapid learning and self-improvement, with particular enthusiasm for staying current with advances in generative AI, LLMs, and machine learning. Evidence of proactive skill development, curiosity about emerging technologies, and willingness to take on challenging new problems.

Skills

Required skills

Machine Learning MathematicsStrong grasp of mathematical foundations including linear algebra, calculus, probability theory, and statistics. Ability to understand model behavior through mathematical analysis and implement algorithms from first principles.
Large Language Model ApplicationsPractical experience with LLM APIs, frameworks, and applications. Understanding of model capabilities, limitations, and techniques for prompt optimization and output quality improvement.
Python ProgrammingProficiency in Python for machine learning development, data manipulation, and scrimentation. Comfortable with scientific computing libraries like NumPy, Pandas, and scikit-learn.
Technical CommunicationAbility to write and communicate technical concepts clearly, evaluate written quality, and assess nuance and accuracy in language. Essential for a knowledge-sharing platform.
Collaborative Problem-SolvingExperience working in teams, contributing to code reviews, and incorporating feedback. Ability to communicate across different technical backgrounds and partner effectively with cross-functional teams.

Nice to have

Production ML SystemsExperience deploying machine learning models to production, including understanding of model serving, monitoring, versioning, and maintenance. Familiarity with MLOps tools and practices.
Agentic AI SystemsHands-on experience building agents that take actions, use tools, or make decisions autonomously. Understanding of agent architecture, reasoning loops, and production deployment challenges.
Experimentation and MetricsExperience designing and analyzing A/B tests, running statistical experiments, and defining meaningful success metrics. Familiarity with experimental design, statistical significance testing, and causal inference.
PyTorch or TensorFlowPractical experience with deep learning frameworks such as PyTorch or TensorFlow. Comfort with model training, debugging, and optimization at scale.
C++ ProgrammingSecondary programming language proficiency in C++ for systems-level optimization, performance-critical components, or infrastructure work.
Quora Platform KnowledgeFamiliarity with Quora's mission to grow the world's collective intelligence and passion for building knowledge-sharing platforms. Understanding of Quora's value proposition and enthusiasm for contributing to the platform.

Compensation & benefits

Salary

USD 107,360 – 152,900 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with competitive coverage options to support your health and wellness needs.

Equity and Stock Options

Equity participation in Quora's growth with refresher equity grants as you grow with the company. Opportunity to benefit from the company's long-term success.

Remote Work Support

Remote work reimbursement to support your home office setup and equipment needs while working from anywhere in Canada or the United States.

Generous Time Off

Paid time off (PTO) to support work-life balance and personal well-being. Country-specific benefits tailored to local regulations.

Employee Assistance Program

Comprehensive employee assistance programs including mental health support, counseling services, and resources for personal and professional development.

Continuous Learning Culture

Culture rooted in learning and improvement with access to professional development resources. Encouraged to experiment with new ideas and technologies in a supportive environment.

Transparent and Inclusive Culture

Work environment built on transparency, idea-sharing, and experimentation. Commitment to diversity and inclusion with equal opportunity employment regardless of background.

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Quora

Quora

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Quora is an American social question-and-answer website and online knowledge market where users ask, answer, and edit questions to share insights and knowledge.

Mountain View, California, United StatesFounded 2009quora.com

Tech Stack

Languages
PythonC++
Frameworks
PyTorchTensorFlowTransformer ModelsLLM Frameworks
Databases
Vector DatabasesRelational Databases
Tools
Jupyter NotebooksGit and Version ControlCI/CD PipelinesMonitoring and Logging ToolsExperiment Tracking
Other
A/B Testing and Statistical AnalysisPrompt EngineeringModel Evaluation FrameworksMLOps and Model ServingScalability and Performance Optimization

Interview Guides

5 guides available for Quora

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