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.
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 ) .
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: d55e44393e6dbcefa6db1e6f7718f27c
- Posted 6 hours ago
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