Search Engineer - Services Special Projects

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.

• 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 (Tensor Flow, 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 Open Search, Elasticsearch, or similar search-based stacks.
• Excellent communication skills and a collaborative mindset.

• 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 Tiger Graph.
• Experience with data and model versioning tools and practices (e.g., DVC, MLflow, Weights & Biases).