Job Description

Our team is building a massive, real-time search experience from the ground up — one that will reach users at Apple scale. It's search at the intersection of Generative AI and Information Retrieval, and it's a rare opportunity to shape a product that millions will rely on.

We are seeking a highly experienced and innovative Search Systems Engineer to help design, develop, and optimize large-scale search systems.

This role is ideal for a technically deep individual who has a strong product sense and enjoys solving real-world problems using modern AI models and scalable systems. We are a passionate team of hardworking engineers and scientists, and we are looking for a strong Search engineer to join us. You will work closely with AI/ML Scientists and engineers at the intersection of Generative AI and Information Retrieval, crafting intelligent systems that personalize user experiences.

Preferred Qualifications

Master's Degree; PhD Preferred
Published work or patents in the domain of search systems, information retrieval, or related ML fields.
Strong foundation in deep learning architectures for search and retrieval (e.g., transformers, graph neural networks, learned sparse representations).
Exposure to multi-objective optimization in search systems (e.g., relevance, diversity, freshness, fairness).
Familiarity with MLOps tools and cloud platforms (AWS/GCP, MLflow, etc.)
Experience with graph databases such as TigerGraph
Experience with data and model versioning tools and practices (e.g., DVC, MLflow, Weights & Biases)

Minimum Qualifications

Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related field
8+ years of experience in Machine Learning, Data Science, or Software Engineering roles with a significant focus on search infrastructure and information retrieval.
Validated experience building and deploying large-scale search systems in production.
Strong proficiency in C++, Go, Python or Java
Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, etc.).
Solid understanding of ML system design, model lifecycle, and experimentation pipelines.
Extensive experience working with large datasets, data processing pipelines (e.g., Spark, Flink), and scalable architectures.
Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
Experience with real-time systems, user feedback loops, and model retraining pipelines.
Vector Infrastructure: Hands-on experience with vector databases such as Milvus, Qdrant, Pinecone, or FAISS.
Working knowledge of cloud environments (AWS or GCP) and containerization (Docker, Kubernetes)
Experience building streaming platforms such as Apache Kafka or comparable message brokers
Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar search-based stacks
Excellent communication skills and a collaborative mindset