Machine Learning 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. Its search at the intersection of Generative AI and Information Retrieval and its a rare opportunity to shape a product that millions will rely are seeking a highly experienced and innovative Search Systems Engineer to help design develop and optimize large-scale search systems. n

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.n

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

Bachelors or Masters degree in Computer Science Machine Learning Statistics or a related fieldn10 years of experience in Machine Learning Data Science or Software Engineering roles with a significant focus on search infrastructure and information on experience building and deploying large-scale search systems in understanding of information retrieval query understanding query augmentation and multi-stage ranking algorithmsnStrong foundation in deep learning architectures for search and retrieval (e.g. transformers cross encoder models graph neural networks learned sparse representations).nExperience with to multi-objective optimization in search systems (e.g. relevance diversity freshness fairness).nExperience with real-time systems user feedback loops and model retraining proficiency in Go Java C and Python nProven experience with ML frameworks including PyTorch with cloud environments (including AWS) and containerization (Docker Kubernetes)nExtensive experience working with data processing pipelines including Spark FlinknHands-on experience with vector search including FAISSnFamiliarity with streaming platforms including Apache KafkanExperience with search infrastructure including OpenSearch and/or ElasticsearchnHands-on experience deploying serving and optimizing LLMs Embeddings and ML models directly in the production query/request pathnPast successful deployments with tuning of models (including quantization) for performance and quality optimizationnExcellent communication skills and a collaborative mindset

Masters Degree; PhD Preferred nPublished work or patents in the domain of search systems information retrieval or related ML with graph databases such as TigerGraphnExperience with data and model versioning tools and practices (e.g. DVC MLflow Weights u0026 Biases)nDeep Experience with KV Stores including SSTables and CassandranExperience with tuning KV-cache and batching for low-latency high-throughput real-time production level experience with inference runtimes/compilers (ONNX Runtime TensorRT/TensorRT-LLM) and serving frameworks (vLLM SGLang or Triton TorchServe ) .