ML Search Engineer
Core
Design and build full-stack systems for intelligent product search that understands intent and learns from behavior to serve millions of industrial buyers.
Role type
Senior Full Stack Engineer (ML Search)
Builds
Scalable retrieval pipelines, APIs, frontend interfaces, and ML inference services for product discovery.
Domain
Industrial e-commerce, AI/ML, Search Systems
Deliverable
production ML models | product features | infrastructure
Required skills
Python, React, Cloud-native architecture, Microservices, RESTful/gRPC APIs, Containerization, Kubernetes, Serverless, SOLID principles, Monorepos, Relational databases, NoSQL databases, Mentorship
Preferred skills
Elasticsearch/OpenSearch/Solr/Algolia, Vector search (FAISS/Pinecone/Weaviate), RAG pipelines, LLM integration, LangChain/LangGraph, Infrastructure-as-code (Terraform/Pulumi)
Technologies
GCP (Cloud Run, Pub/Sub, GKE, Cloud Functions), Python, React, Next.js, Vite.js, Remix.js, Gatsby.js, Elasticsearch, Pinecone, Weaviate, FAISS, PostgreSQL, MySQL, MongoDB, DynamoDB, Docker, Kubernetes, Terraform, Pulumi
Responsibilities
Design, develop, and deploy production Python services for retrieval and ranking pipelines; Build and integrate ML inference pipelines including embedding models and reranking services; Develop event-driven, real-time architectures using GCP services; Write clean, well-tested, observable Python backends and own services through deployment and monitoring; Drive frontend architecture decisions and create reusable component libraries; Work with Search and ML Architects to implement hybrid retrieval systems combining keyword and vector search; Build and maintain Elasticsearch indexing pipelines and relevance tuning tooling; Instrument search pipelines with metrics like CTR and latency; Champion CI/CD, observability, testing, and infrastructure-as-code; Lead design sessions and translate product requirements into technical solutions; Provide mentorship to mid-level and junior developers.
Seniority
Senior, hands-on IC
