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 ) .