# Machine Learning Engineer, New Grad - Quora (Remote) **Company:** [Quora](https://scaleengineer.com/companies/quora) Machine Learning Engineer, New Grad position at Quora focused on designing and deploying LLM-powered agents for content quality and safety across Quora's platform. You'll work with a small, collaborative ML team on challenging problems impacting hundreds of millions of users, leveraging production machine learning systems including over 100 active models. This role requires strong foundation in machine learning algorithms, Python proficiency, and hands-on experience with transformer models or LLM applications, with no prior trust and safety experience required. **Role:** ML Engineer **Seniority:** Junior **Locations:** Remote - Multiple Locations **Remote:** yes **Salary:** 107360–152900 USD [Apply](https://jobs.ashbyhq.com/quora/40659fe7-545e-4b08-b6d7-2567b18b9ed3) Canonical: https://scaleengineer.com/jobs/quora/machine-learning-engineer-new-grad-quora-remote-40659fe7 --- ## Responsibilities - Design, Build, and Deploy LLM-Powered Agents: Architect and implement large language model-based agents to detect and address spam, harassment, and policy-violating content at Quora's scale. Take ownership of end-to-end system development from conceptualization through production deployment, ensuring robust handling of millions of daily content moderation decisions. - Develop Agentic Workflows for Content Moderation: Create and optimize autonomous workflows that automate complex moderation tasks, improving both efficiency and effectiveness of content and user safety systems. Design workflows that can handle dynamic escalation patterns and adapt to emerging trust and safety challenges. - Design Adaptive ML Systems: Build machine learning systems that continuously adapt to evolving abuse patterns, new policy violations, and emerging safety challenges. Implement monitoring and feedback mechanisms to ensure systems remain effective as adversarial tactics evolve. - Evaluate and Monitor Agent Performance: Conduct comprehensive performance evaluations through offline metrics, online A/B experiments, and production monitoring dashboards. Balance multiple optimization objectives including accuracy, reliability, latency, and computational cost while maintaining system quality standards. - Investigate and Iterate on Failure Cases: Analyze production failures and edge cases to understand root causes of misclassifications. Experiment with novel approaches and iteratively improve system robustness, documentation, and deployment procedures based on real-world performance data. - Identify ML Opportunities for Product Impact: Proactively discover and propose new applications of machine learning and large language models to content quality and safety challenges across the Quora platform. Contribute strategic insights on how ML can solve emerging product and safety priorities. - Collaborate and Take Ownership: Work closely with experienced machine learning engineers and cross-functional teams throughout the full project lifecycle. Take meaningful ownership of projects from early research and development phases through successful production deployment and ongoing maintenance. ## Requirements ### education - {"name":"Computer Science, Engineering, or Related Technical Degree","description":"Bachelor's degree (B.S.), Master's degree (M.S.), or Doctorate (Ph.D.) in Computer Science, Engineering, Mathematics, Physics, or related technical field. Degree completion or currently in progress by start date (early 2027)."} ### technical - {"name":"Mathematical Foundations of Machine Learning","description":"Deep understanding of core ML algorithms including supervised learning, unsupervised learning, neural networks, and optimization techniques. Strong grasp of linear algebra, calculus, and probability theory as applied to machine learning systems."} - {"name":"LLM Applications and Transformer Models","description":"Hands-on experience with transformer-based architectures and large language model applications through coursework, research projects, or production implementations. Understanding of attention mechanisms, fine-tuning approaches, and LLM behavior in real-world applications."} - {"name":"Python Programming","description":"Strong proficiency in Python including data structures, algorithms, functional programming, and object-oriented design. Ability to write clean, maintainable, production-quality code with proper error handling and testing practices."} - {"name":"Coordination Hours Availability","description":"Must be available for meetings, synchronous collaboration, and impromptu communication during Quora's coordination hours: Monday-Friday, 9am-3pm Pacific Time. This requirement ensures effective real-time collaboration with the distributed team."} ### experience - {"name":"Software Engineering Foundation","description":"Preferred background in practical software engineering through internships, professional work experience, open-source contributions, or competitive programming participation. Demonstrates ability to write production-quality code and work in engineering teams."} - {"name":"LLM Agent Development Experience","description":"Preferred hands-on experience building and evaluating large language model agents in projects, academic research, internship roles, or production systems. Understanding of agentic design patterns, evaluation methodologies, and deployment considerations."} - {"name":"Natural Language Processing (NLP) Background","description":"Valuable coursework or project experience in natural language processing, text classification, sequence modeling, or language understanding tasks. Knowledge of NLP techniques and their application to real-world problems."} - {"name":"AI Coding Tool Proficiency","description":"Regular use and proficiency with modern AI-assisted coding tools such as Claude Code, GitHub Copilot, or OpenAI Codex. Demonstrates comfort working with AI augmentation in the development workflow."} - {"name":"Content Safety and Trust Interest","description":"Genuine interest in content moderation, platform safety, trust and safety engineering, or related domains. Passion for solving complex societal challenges around harmful content detection and mitigation."} ## Skills ### required - {"name":"Machine Learning Algorithms","description":"Comprehensive understanding of classification, regression, clustering, and deep learning algorithms. Ability to select appropriate algorithms for specific problems and understand their mathematical foundations and trade-offs."} - {"name":"Python Development","description":"Advanced Python programming skills with experience in data structures, functional paradigms, and building scalable systems. Proficiency with scientific Python libraries and writing production-grade code."} - {"name":"Transformer Architecture Knowledge","description":"Understanding of transformer models and attention mechanisms used in modern LLMs. Knowledge of how these architectures work and their applications to NLP and content understanding tasks."} - {"name":"Deep Learning Frameworks","description":"Practical experience with PyTorch and/or TensorFlow for implementing, training, and deploying neural networks. Ability to work with distributed training and model optimization."} - {"name":"Experimental Design and Evaluation","description":"Experience designing experiments to validate ML systems, measuring performance metrics, conducting offline evaluations, and running online experiments. Understanding of statistical significance and metric selection."} ### preferred - {"name":"LLM Agent Systems","description":"Experience building autonomous agents powered by large language models, including planning, reasoning, tool use, and self-correction mechanisms. Understanding of agentic design patterns and evaluation strategies."} - {"name":"Production Machine Learning","description":"Exposure to deploying ML systems in production environments, monitoring model performance, handling model drift, and maintaining systems at scale. Understanding of MLOps and engineering best practices."} - {"name":"Distributed Computing","description":"Experience with distributed data processing frameworks like Apache Spark or similar technologies for handling large-scale datasets and computations."} - {"name":"Content Moderation ML","description":"Familiarity with machine learning approaches to content moderation, spam detection, or abuse prevention. Understanding of the nuances and challenges in building fair and effective safety systems."} - {"name":"AI-Assisted Development","description":"Comfort and proficiency using modern AI coding assistants like Claude Code or Codex in daily development workflows. Ability to effectively leverage AI tools for rapid prototyping and development."} - {"name":"Natural Language Processing","description":"Coursework or practical experience in NLP techniques including text preprocessing, embeddings, language models, and sequence-to-sequence architectures for understanding and generating text."} ## Tech stack ### tools - {"name":"Kubernetes","description":"Container orchestration platform for deploying and managing ML models and services in production"} ### others - {"name":"LLM Agents","description":"Large language model-based agents for automating content moderation and policy enforcement"} - {"name":"Transformer Models","description":"State-of-the-art neural architectures for natural language understanding and generation tasks"} - {"name":"Agentic Workflows","description":"Autonomous workflow systems that automate complex decision-making and task execution"} ### databases - {"name":"Spark","description":"Distributed computing framework for large-scale data processing and analytics at Quora's scale"} ### languages - {"name":"Python","description":"Primary programming language for machine learning development, model implementation, and data processing pipelines"} ### frameworks - {"name":"PyTorch","description":"Deep learning framework used for building and training neural networks and LLM-powered systems"} - {"name":"TensorFlow","description":"Machine learning framework for designing and deploying scalable ML models in production environments"} ## Benefits ### benefits - {"name":"Comprehensive Health Coverage","description":"Medical, dental, and vision insurance coverage with company contributions to ensure employee wellness and healthcare access"} - {"name":"Equity Compensation","description":"Equity refreshers and flexible equity program options where a portion of equity compensation may be taken as cash in equity-eligible countries, enabling long-term wealth building"} - {"name":"Remote Work Benefits","description":"Remote work reimbursement and support for home office setup, recognizing Quora's remote-first culture and employee work-from-home needs"} - {"name":"Paid Time Off","description":"Generous paid time off policy supporting work-life balance and employee wellbeing across the calendar year"} - {"name":"Employee Assistance Programs","description":"Comprehensive employee assistance programs providing support for mental health, financial wellness, and life challenges"} - {"name":"Country-Specific Benefits","description":"Additional benefits tailored to local markets and regulations, varying by country of employment to ensure competitive total compensation"} ## Compensation - **max:** 152900 - **min:** 107360 - **currency:** USD - **stockOptions:** true ## Interview process ### steps - {"name":"Application Review","description":"Initial screening of applications with AI assistance in sorting and initial evaluation. All decisions made by human team members. Review of resume, cover letter, and relevant coursework or projects demonstrating ML fundamentals and LLM experience."} - {"name":"Technical Assessment","description":"Evaluation of machine learning knowledge, Python coding skills, and understanding of transformer models. May include take-home coding challenges, problem-solving exercises, or written assessments testing algorithmic thinking and ML concepts."} - {"name":"ML Systems Design Interview","description":"Discussion of how to approach designing machine learning systems for content safety and moderation at scale. Assessment of ability to think about trade-offs, evaluation metrics, and system architecture decisions relevant to production ML systems."} - {"name":"Team and Culture Fit Discussion","description":"Conversation with team members about collaboration style, learning approach, and alignment with Quora's mission to grow collective intelligence. Exploration of passion for content safety, trust and safety challenges, and working in a remote-first environment."} - {"name":"Background Check and Identity Verification","description":"For final candidates, comprehensive background check and identity verification process to ensure secure hiring. All checks completed prior to offer finalization and onboarding."} ## Full description _\[_[_**Quora is a privately held, "remote-first" company**_](https://www.quora.com/q/quora/Remote-First-at-Quora)_**.** This position can be performed remotely from anywhere in the US or Canada. Please visit_ [_careers.quora.com/eligible-countries_](http://careers.quora.com/eligible-countries) _for details regarding employment eligibility by country.\]_ ## **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:** Our ML team is small and highly collaborative, working on challenging problems that impact hundreds of millions of Quora users. We encourage engineers to think big, experiment with new ideas, and continuously learn and improve. With over 100 ML models in production, engineers have opportunities to take ownership of meaningful projects, work closely with experienced teammates, and quickly see the impact of their work. We're building the next generation of AI-powered systems to improve content quality and safety across Quora. By leveraging LLMs and agentic workflows, we're transforming how we detect harmful content, automate complex decisions, and respond to evolving challenges at scale. As a **Machine Learning Engineer (New Grad)**, you'll work alongside experienced engineers to design, evaluate, and deploy these systems in production. **No previous trust and safety experience is required.** You'll learn from the engineers who built these systems. **Stack:** Python, PyTorch, TensorFlow, Spark, Kubernetes ## **Responsibilities:** * Design, build, and deploy LLM-powered agents to detect and address spam, harassment, and other policy-violating content at Quora's scale * Develop agentic workflows to automate complex tasks, improving the efficiency and effectiveness of our content and user moderation systems * Design and improve ML systems that adapt to evolving abuse patterns and new challenges * Evaluate agent performance through offline evaluations, online experiments, and production monitoring, balancing accuracy, reliability, latency, and cost * Investigate failure cases, experiment with new approaches, and iterate on solutions to improve system performance * Identify new opportunities to apply ML and LLMs to content quality and safety across Quora * Collaborate with experienced engineers and take ownership of projects from early development through production ## **Minimum Requirements:** * **Availability for meetings and impromptu communication during Quora's “**[**coordination hours**](https://quora.com/coordination%5Fhours)**" (Mon-Fri: 9am-3pm Pacific Time)** * B.S., M.S., or Ph.D. in Computer Science, Engineering or a related technical field, completed or in progress * Strong understanding of mathematical foundations of Machine Learning algorithms * Coursework, research, or project experience with LLM applications or transformer models * Strong knowledge of Python * A passion for learning and always improving yourself and the team around you * **Able to start full-time in early 2027** ## **Preferred Requirements:** * Previous software engineering experience via an internship, work experience, open-source contribution or coding competition * Hands-on experience building and evaluating LLM agents, in projects, research, internships, or production * Regular use of AI coding tools such as Claude Code or Codex * Coursework or projects in natural language processing (NLP) * Genuine interest in content safety and quality * 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 $107,360 - $152,900 USD + equity + benefits. * Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $137,852 - $157,061 CAD + equity + benefits. For all other locations in Canada, the salary range is $128,661 - $146,590 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-JC1 #LI-REMOTE