Machine Learning Engineer, Recommendation Systems - Quora; Remote
New York, New York City, Richmond County, New York, USA
Listed on 2026-11-06
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Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Location: New York
We are looking for a Staff Machine Learning Engineer to define the technical future of Quora’s recommendation ecosystem. In this role, you will own the technical direction for feed ranking and retrieval, lead our highest-impact modeling bets, and pioneer the integration of LLMs and generative models to redefine how we understand and rank content. Beyond the home feed, you will also scale recommendation surfaces like notifications and digest emails, while architecting the underlying tools and abs
- Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
- + years of professional software development experience in machine learning
- + years of professional experience building and optimizing Home Feed recommendation systems for social media and user-generated content platforms.
- Deep knowledge of the full recommender stack: candidate generation, retrieval, ranking, re-ranking, embeddings, and multi-objective optimization.
- Deep understanding of mathematical foundations of machine learning algorithms
Relevance is based on skills and concepts, not a guarantee that you qualify.
Covered skills: AI fundamentals Machine learning Experimentation
Skills to close:
None listed
Coming-soon courses matched to this posting: outline, waitlist, and skill path.
1. Text Representation and Semantic SearchConvert human language into computational representations using embeddings and build semantic search systems with vector databases.
2. Retrieval-Augmented Generation PipelinesBuild systems that ground model outputs in retrieved context rather than relying on memorized training data.
3. Evaluation Frameworks for Production NLPDesign test suites that measure accuracy, safety, robustness, and bias before and after deployment.
4. Safe Input Handling and GuardrailsProtect systems from ambiguous, adversarial, and risky user inputs with validation and fallback strategies.
5. Monitoring and Debugging NLP in ProductionInstrument systems to detect failures, track performance drift, and build feedback loops for continuous improvement.
6. Cost Control and Latency OptimizationOptimize NLP systems for speed and budget without sacrificing reliability or user experience.
7. Domain-Specific NLP ApplicationsApply production NLP patterns to customer operations, compliance, knowledge work, and field applications.
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