Software Engineer, Machine Learning Platform, New Grad
New
Q
QuoraMachine Learning Infrastructure
This position can be performed remotely from anywhere in Canada or the United States., Availability for meetings and impromptu communication during Quora's "coordination hours" (Mon-Fri: 9am-3pm Pacific Time)Full-TimeEntry
SalaryUS candidates: $97,600 - $139,000 USD. Toronto/Vancouver: $125,320 - $142,783 CAD. Other Canada: $116,965 - $133,264 CAD. All include equity + benefits.
Apply NowOpens the employer's application page
Job Details
- Experience
- A 2025 or 2026 graduate
- Required Skills
- AWSPythonKubernetesMachine LearningPyTorchC++GoDistributed Systems
Requirements
- Availability for meetings and impromptu communication during Quora's "coordination hours" (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: Previous software engineering experience via internship, open-source, or coding competitions
- Preferred: Coursework or hands-on experience with ML frameworks such as PyTorch or TensorFlow
- Preferred: Exposure to Kubernetes, Docker, or cloud technologies like AWS
- Preferred: Experience with low-level performance work like profiling, benchmarking, or optimization
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 (LRM) to Large Language Models (LLM)
- 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
View Full Description & ApplyYou'll be redirected to the employer's site
