Senior Machine Learning Engineer, Search Relevance

Box•Redwood City, CA

11h•$211,000 - $263,500•Hybrid

About The Position

Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia. By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift. The Search Relevance team at Box 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’re investing 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’ll be building 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. We’re looking for a Senior Software Engineer to elevate search quality end-to-end—signals, ranking, retrieval, and evaluation—while building scalable, low-latency services that serve queries in real time. You’ll partner with Product, Data, and Infra teams to productionize cutting-edge models and experimentation frameworks, and help define the future of Box’s content intelligence, including hybrid and semantic search and our next-generation content agent.

Requirements

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++, or 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.

Nice To Haves

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 TensorFlow 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.

Responsibilities

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 productionize 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, available at all times while on-call to help respond and triage any issues that arise.