Machine Learning/ Search Engineer - Services Special Projects

Summary

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.

Description

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.

Responsibilities

• Design, build, and maintain large-scale, low-latency, high-performance search systems that can scale.
• Develop and optimize ranking, relevance, and retrieval through ML/AI models and merging traditional keyword search with vector-based semantic search using embedding models and vector databases.
• Develop sophisticated NLP pipelines for intent classification, entity extraction, semantic parsing, and query expansion.
• Merge traditional keyword search (BM25) with vector-based semantic search using embedding models and vector databases.
• Design and Implement machine learning models (e.g. Learning to Rank, Cross Encoder based models) and multi-stage reranking algorithms to optimize search precision and recall.
• Build offline and online evaluation metrics, A/B testing frameworks, and continuous improvement strategies for search quality
• Partner with Research Scientists, Product, Data Engineering, MLOps, Search Infrastructure teams, and UX to align search features with business and user goals.
• Stay current with the latest research and innovations in search and information retrieval technologies, translating them into scalable production systems.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related field
• 10+ years of experience in Machine Learning, Data Science, or Software Engineering roles with a significant focus on search infrastructure and information retrieval.
• Hands on experience building and deploying large-scale search systems in production.
• Deep understanding of information retrieval, query understanding, query augmentation and multi-stage ranking algorithms
• Strong foundation in deep learning architectures for search and retrieval (e.g., transformers, cross encoder models, graph neural networks, learned sparse representations).
• Experience with to multi-objective optimization in search systems (e.g., relevance, diversity, freshness, fairness).
• Experience with real-time systems, user feedback loops, and model retraining pipelines.
• Strong proficiency in Go, Java, C++ and Python
• Proven experience with ML frameworks including PyTorch, XGBoost.
• Familiarity with cloud environments (including AWS) and containerization (Docker, Kubernetes)
• Extensive experience working with data processing pipelines including Spark, Flink
• Hands-on experience with vector search including FAISS
• Familiarity with streaming platforms including Apache Kafka
• Experience with search infrastructure including OpenSearch, and/or Elasticsearch
• Hands-on experience deploying, serving, and optimizing LLMs, Embeddings and ML models directly in the production query/request path
• Past successful deployments with tuning of models (including quantization) for performance and quality optimization
• Excellent communication skills and a collaborative mindset

Preferred Qualifications

• Master's Degree; PhD Preferred
• Published work or patents in the domain of search systems, information retrieval, or related ML fields.
• Experience with graph databases such as TigerGraph
• Experience with data and model versioning tools and practices (e.g., DVC, MLflow, Weights & Biases)
• Deep Experience with KV Stores including SSTables and Cassandra
• Experience with tuning KV-cache and batching for low-latency, high-throughput real-time inference.
• Deep production level experience with inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), and serving frameworks (vLLM, SGLang or Triton, TorchServe ) .