Machine Learning Engineer, New Grad - Quora

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Machine Learning Engineer, New Grad – Quora based in the United States. Launch your machine learning career by helping build AI-powered systems that improve content quality and safety across a global knowledge-sharing platform. In this role, you will collaborate with experienced engineers to design, develop, evaluate, and deploy large language model (LLM)-powered agents that identify harmful content, detect abuse, and automate complex moderation tasks. You will work on challenging machine learning problems at scale, with opportunities to experiment with emerging AI technologies and contribute to systems serving millions of users. As part of a small, collaborative engineering team, you will take ownership of meaningful projects and follow them from early development through production deployment. You will evaluate model performance, investigate failure cases, and continuously improve the accuracy, reliability, and efficiency of AI-driven solutions. This remote opportunity is ideal for a recent graduate or emerging engineer with a strong technical foundation, a curiosity for AI innovation, and a passion for building safer online experiences. \n Accountabilities

Design, build, and deploy LLM-powered agents that detect and address spam, harassment, and other policy-violating content across a large-scale online platform. Develop agentic workflows that automate complex content moderation and user safety tasks, improving the efficiency, consistency, and effectiveness of existing systems. Build and enhance machine learning models and AI-powered systems that adapt to evolving abuse patterns, emerging threats, and changing content safety challenges. Evaluate model and agent performance through offline testing, online experiments, and production monitoring, balancing accuracy, reliability, latency, and computational costs. Investigate model failures, analyze edge cases, conduct experiments, and refine technical approaches to improve system quality and overall performance. Identify new opportunities to apply machine learning, large language models, and emerging AI techniques to content quality, platform integrity, and user safety. Collaborate closely with experienced machine learning engineers and cross-functional colleagues to develop practical solutions to complex technical challenges. Take ownership of projects throughout the engineering lifecycle, from problem identification and experimentation to implementation, deployment, and ongoing optimization. Contribute to a culture of continuous learning, technical experimentation, knowledge sharing, and collaborative problem-solving. Help develop scalable, reliable, and cost-effective AI systems that support a positive user experience and protect the integrity of a global knowledge-sharing community. Requirements

A bachelor's, master's, or Ph.D. degree in Computer Science, Engineering, or a related technical field, either completed or currently in progress. Strong understanding of the mathematical foundations underlying machine learning algorithms and their practical applications. Coursework, research, or project experience involving large language model applications, transformer architectures, or related AI technologies. Strong programming skills in Python and the ability to develop, test, and maintain reliable code. Genuine enthusiasm for learning, experimenting with new technologies, and continuously improving both individual and team performance. Availability to begin full-time employment in early 2027. Ability to participate in meetings and spontaneous collaboration during the team's coordination hours, Monday through Friday, from 9:00 a.m. to 3:00 p.m. Pacific Time. Previous software engineering experience through an internship, professional role, open-source contribution, or coding competition is preferred. Practical experience building, implementing, or evaluating LLM-powered agents through academic projects, research, internships, or production systems is an advantage. Familiarity with AI-assisted coding tools, such as Claude Code or Codex, preferably through regular hands-on use. Coursework, research, or project experience in natural language processing (NLP) is beneficial. Familiarity with machine learning frameworks and infrastructure, including PyTorch, TensorFlow, Spark, or Kubernetes, is an advantage. A genuine interest in content safety, online community quality, and the responsible application of artificial intelligence. Strong analytical thinking, curiosity, and problem-solving skills, with the ability to investigate technical issues and develop evidence-based solutions. A collaborative mindset and willingness to learn from experienced colleagues, share ideas, and contribute to a high-performing engineering team. An interest in supporting a mission centered on expanding access to knowledge and creating positive online experiences. Candidates from diverse backgrounds are encouraged to apply, including those who may not meet every preferred qualification but demonstrate strong technical potential and a passion for machine learning. Benefits

Competitive U.S. salary:

Annual base compensation ranging from $107,360 to $152,900 USD, depending on experience, education, location, and business needs. Equity compensation:

Eligibility for equity awards and refreshers, with a flexible equity program available in eligible countries that may allow a portion of equity compensation to be received as cash. Comprehensive healthcare:

Medical, dental, and vision coverage. Remote-first work environment:

Flexibility to work remotely from eligible locations within the United States. Remote work reimbursement:

Financial support to help cover eligible remote-working expenses. Paid time off:

Time away from work to support rest, personal commitments, and work-life balance. Employee assistance programs:

Resources designed to support employee well-being. Professional development:

Opportunities to learn from experienced machine learning engineers and contribute to challenging, real-world AI projects. Hands-on AI innovation:

Exposure to large language models, agentic workflows, production machine learning systems, and modern engineering tools. Meaningful technical impact:

The opportunity to develop AI systems that improve content quality, detect harmful behavior, and help create safer online communities. Inclusive culture:

A collaborative environment that values diverse perspectives, experimentation, transparency, and continuous learning. \n

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