Machine Learning/ Search Engineer - Services Special Projects

Socket.dev; Cupertino (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Socket.dev in Cupertino is building a real-time search experience at massive scale, blending Generative AI with Information Retrieval. We are seeking a highly experienced Search Systems Engineer to design, develop, and optimize distributed search systems in production.

You will own indexing, ranking, embeddings in the query path, and collaborate with ML teams to deploy models with low latency. Requirements include 10+ years in ML/DS/SE, strong IR fundamentals, and proficiency in Go/Java/C++ and

Qualifications

• Bachelor's or Master's degree in Computer Science, ML, Statistics, or related field with strong emphasis on search infrastructure.
• 10+ years in ML, data science, or software engineering roles focused on search, with hands-on production experience.
• Deep understanding of information retrieval, query understanding, augmentation, and multi-stage ranking.
• Experience with real-time systems, model retraining pipelines, and distributed search stacks.

Responsibilities

• Design, build, and optimize large-scale search systems used in production.
• Develop ranking models, embeddings, and multi-objective optimization for relevance, diversity, and freshness.
• Deploy and monitor ML models in live query paths with low latency and high throughput.

Skills

Large-scale search

Information retrieval

Query understanding

Go

Java

C++

Python

PyTorch

XGBoost

Spark

Kubernetes

OpenSearch

FAISS

LLMs in production

Vector search

Streaming pipelines

Education

Bachelor's degree

Master's degree

Tools

Docker

Kubernetes

Spark

Kafka

FAISS

ONNX Runtime

TensorRT

TorchServe

OpenSearch

MLflow

Weights & Biases

Job description

DESCRIPTION

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.

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 ) .