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. 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 ) .