Machine Learning Engineer, New Grad
New
Q
QuoraContent safety
This position can be performed remotely from anywhere in the US or Canada., Availability for meetings and impromptu communication during Quora's “coordination hours” (Mon-Fri: 9am-3pm Pacific Time)Full-TimeEntry
SalaryUS 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.
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Job Details
- Required Skills
- PythonKubernetesMachine LearningPyTorchSparkTensorflowNLP
Requirements
- Have a B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field, completed or in progress.
- Have a strong understanding of the mathematical foundations of machine learning algorithms.
- Have coursework, research, or project experience with LLM applications or transformer models.
- Have strong knowledge of Python.
- Be available for meetings and impromptu communication during Monday–Friday, 9 a.m.–3 p.m. Pacific Time coordination hours.
- Be able to start full-time in early 2027.
- Preferred: Have software engineering experience through an internship, work experience, open-source contribution, or coding competition.
- Preferred: Have hands-on experience building and evaluating LLM agents in projects, research, internships, or production.
- Preferred: Regularly use AI coding tools such as Claude Code or Codex.
- Preferred: Have coursework or projects in natural language processing.
Responsibilities
- Design, build, and deploy LLM-powered agents to detect and address spam, harassment, and other policy-violating content.
- Develop agentic workflows to automate complex tasks in content and user moderation systems.
- Design and improve machine learning systems that adapt to evolving abuse patterns and new challenges.
- Evaluate agent performance through offline evaluations, online experiments, and production monitoring.
- Balance accuracy, reliability, latency, and cost when evaluating system performance.
- Investigate failure cases, experiment with new approaches, and iterate on solutions.
- Identify opportunities to apply machine learning and LLMs to content quality and safety.
- Collaborate with experienced engineers and own projects from early development through production.
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