Senior Software Engineer, Search Relevance

Why Box Needs You

The Search Relevance team powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. As we expand into a new era of AI‑powered content understanding, we invest in the foundation that makes great search possible: reliable systems, strong signals, and models that learn from real‑world usage.

This is a rare opportunity to work at the intersection of information retrieval science, applied machine learning, and large‑scale distributed systems. You will build the infrastructure that powers intelligent content discovery for Fortune 500 companies – where milliseconds matter, relevance is measurable, and your experiments directly impact how millions of users work.

• Build and improve ranking, retrieval, and recommendation systems; identify the right signals and metrics to drive quality improvements that users can feel.
• Apply cutting‑edge techniques (embeddings, LLM‑enabled retrieval, hybrid search) to product ionize experimentation and evaluation pipelines that scale to trillions of documents.
• Define and execute offline/online evaluation, A/B testing, and relevance tuning (NDCG, MRR, precision@k) to continuously improve search outcomes.
• Develop infrastructure for low‑latency, high‑availability query serving and near real‑time indexing across distributed systems.
• Tackle distributed systems challenges including data sharding, intelligent routing, replication, and performance optimization.
• From ETL pipelines and feature engineering to model serving and result ranking, understand how data flows through the system and optimize at every stage.
• Lead design and implementation of new platform components from the ground up; establish patterns, raise the bar on code quality, and champion best practices.
• Share your expertise, contribute to technical direction, conduct thoughtful code reviews, and help shape our engineering culture.
• Participate in our on‑call rotation and be available at all times while on‑call to respond to and triage any issues that arise.

• 5+ years of industry experience building and operating backend or distributed systems at scale.
• Strong proficiency in an object‑oriented language (e.g., Java, Scala, C++, Python);
  Python experience strongly preferred.
• Hands‑on experience building ranking, recommendation, NLP, or applied AI platforms in production. You understand the ML lifecycle from training to serving.
• Comfortable with data pipelines, message queues, and/or streaming systems (e.g., Kafka, Pub/Sub) and near real‑time data processing.
• Experienced deploying and operating microservices in cloud environments; solid grasp of reliability, observability, and performance best practices.
• BS in Computer Science or related field, or equivalent practical experience.
• AI‑first mindset – pragmatic about using the right models, signals, and evaluation methods to improve outcomes quickly and measurably.

• Experience with Elasticsearch, Solr, Lucene, or building custom search systems; deep understanding of inverted indexes, scoring functions, and query optimization.
• Knowledge of ML relevance tuning, learning‑to‑rank, retrieval evaluation metrics, offline/online testing, and A/B experimentation.
• Experience with vector search, dense/sparse embeddings, semantic retrieval, and hybrid search architectures.
• Familiarity with IR fundamentals – BM25, TF‑IDF, query understanding, intent classification, and multi‑stage retrieval pipelines.
• Familiarity with Kubernetes, Terraform, and major cloud platforms (GCP, AWS, or Azure).
• Practical experience with PyTorch or Tensor Flow for training and fine‑tuning models; LLM familiarity helpful but not required.
• Experience building feature stores, real‑time feature computation, and online/offline feature consistency.

Box lives its values, with community and in‑person collaboration being a core part of our culture. Boxers are expected to work from their assigned office a minimum of 3 days per week. Your recruiter will share more about how we work and company culture during the hiring process.

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