Machine Learning Engineer, Ranking & Retrieval

Accountabilities: Own the complete machine learning lifecycle for ranking and retrieval models, including training, deployment, production serving, monitoring, and ongoing improvement.

Build ranker features, training pipelines, and offline evaluation frameworks that enable reliable experimentation and measurable improvements in search relevance.

Design, develop, and scale hybrid retrieval systems combining lexical and vector search, including large-scale HNSW implementations with disk offloading.

Develop and operate embedding inference systems capable of processing billions of documents and supporting high-volume retrieval workloads.

Improve query understanding through intent modeling, query expansion, and other techniques that help users retrieve more relevant information.

Build permission-aware retrieval systems that respect multi-tenant boundaries and ensure users only access content they are authorized to retrieve.

Create measurement and evaluation frameworks to assess search quality, identify weaknesses, and guide continuous ranking and retrieval improvements.

Partner with Search Infrastructure, AI, and backend engineering teams to integrate ML-driven ranking and retrieval capabilities across the broader platform.

Requirements:

Bachelor’s degree in Computer Science, Machine Learning, or a related technical field.

5+ years of machine learning engineering experience focused on ranking, retrieval, information retrieval, or closely related areas.

Proven experience owning the full ML lifecycle, including model training, deployment, production serving, and optimization.

Hands-on experience training ranking models, including feature engineering, training pipelines, and offline evaluation.

Strong experience building hybrid retrieval systems that combine lexical and vector search.

Experience operating embedding inference at significant scale, ideally across very large document collections.

Strong fundamentals in query understanding, including intent modeling and query expansion.

Experience with permission-aware retrieval and multi-tenant architectures is preferred.

Experience indexing large-scale user-generated content rather than small or static datasets is advantageous.

Hands-on experience with OpenSearch or Elasticsearch, including sharding, index management, and real-time ingestion at scale, is a strong plus.

Background in NLP, semantic search, or agentic retrieval is desirable.

Experience with TypeScript in backend systems is an additional advantage.

Strong analytical and problem-solving skills, with the ability to work effectively on complex, large-scale search problems.

Collaborative mindset and strong communication skills, with the ability to work across infrastructure, AI, and backend engineering teams.

Benefits:

Remote work opportunity.

Opportunity to work on large-scale ranking and retrieval systems serving millions of users.

Significant ownership across the full machine learning lifecycle, from experimentation to production.

Exposure to advanced AI, semantic search, vector retrieval, query understanding, and large-scale embedding infrastructure.

Opportunity to contribute directly to the evolution of an AI-native productivity platform.

Collaborative environment working closely with Search Infrastructure, AI, and backend engineering teams.

Competitive compensation and benefits are provided according to the applicable employment package; the source posting does not specify a salary range or detailed benefits package.

Visa sponsorship for engineering and product roles may be considered based on specific business needs, but sponsorship is not guaranteed.

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