Machine Learning Engineer III, Search Relevance

About the role

The Search Relevance team at Box powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. This role focuses on improving search quality end-to-end, including signals, ranking, retrieval, and evaluation.

Responsibilities

• Design, build, and iterate on components for ranking, retrieval, and recommendations that improve measurable relevance and latency.
• Implement production features leveraging embeddings, semantic/hybrid search, and LLM-enabled retrieval under mentorship and design guidance.
• Contribute to offline/online evaluation, A/B tests, and relevance tuning using metrics such as NDCG, MRR, and precision@k.
• Develop reliable, observable microservices and near real-time indexing pipelines across distributed systems.
• Own well-scoped projects from design to rollout, writing clear design docs, tests, and operational runbooks.
• Improve data and feature pipelines (batch/streaming) to ensure quality, freshness, and end-to-end performance.
• Document patterns and contribute to team best practices that raise the bar on code quality and reliability.
• Participate in our on-call rotation, available at all times while on-call to help respond to and triage any issues that arise.

Requirements

• 3+ years of industry experience building backend or distributed systems, with production ownership of services or data pipelines.
• Proficient in at least one of: Java, Scala, C++, or Python; comfortable writing production-grade Python is a plus.
• Exposure to search, ranking, recommendations, or applied ML in production; understand the basics of training-to-serving workflows.
• Experience with data pipelines, message queues, or streaming systems (e.g., Kafka, Pub/Sub) and near real-time processing.
• Familiarity with cloud-native microservices, CI/CD, observability, and performance tuning.
• BS in Computer Science or related field, or equivalent practical experience.
• Pragmatic, metrics-driven mindset—eager to experiment, measure impact, and iterate quickly in collaboration with partners.

Qualifications

• Experience with Elasticsearch, Solr, Lucene, or custom search systems; understanding of inverted indexes and scoring functions.
• Knowledge of relevance tuning, learning-to-rank concepts, and offline/online experimentation practices.
• Familiarity with vector search, dense/sparse embeddings, and hybrid retrieval architectures.
• Experience with Kubernetes/Terraform and a major cloud (GCP/AWS/Azure).
• Practical exposure to PyTorch or TensorFlow; LLM familiarity helpful but not required.

Skills

• Strong programming skills in Java, Scala, C++, or Python.
• Experience with distributed systems and microservices architecture.
• Understanding of search, ranking, and recommendation systems.
• Knowledge of cloud-native technologies and CI/CD practices.
• Experience with data pipelines and streaming systems.
• Ability to work collaboratively with cross-functional teams.

Benefits

Box offers a comprehensive benefits package including health insurance, retirement plans, paid time off, and more. The company also provides a flexible work environment and encourages professional growth through training and development programs.

Pay

$175,500 - $219,500 USD

Schedule

Full-time position with a minimum of 3 days per week in the office.