ML Search Engineer

ABOUT THE ROLE

We're building intelligent product search that understands intent, learns from behavior, and gets smarter over time. As a SeniorFull StackEngineer on the ML/AI Search team, you'll design and buildboththefrontend and backendsystems that power product discovery for millions of industrial buyers—from scalable retrieval pipelines,APIs, and Frontendinterfaces that make AI accessible.

This isn't a research role. You'll own the full lifecycle: prototyping ideas, shipping production-grade services on GCP, and iterating based on real user data. Strong Pythonand Reactengineeringisthe foundation—if you also bring experience in search systems, vector databases, or Elasticsearch, you'll hit the ground running from day one.

WHAT YOU'LL DO

Build & Ship Search and AI-Powered Systems

• Design, develop, and deploy production Python services end-to-end—from retrieval and ranking pipelines through client-facing APIs.

• Build and integrate ML inference pipelines: embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services.

• Develop event-driven, real-time architectures using GCP services—Cloud Run, Pub/Sub, GKE, Cloud Functions.

• Write clean, well-tested, observable Python backends; own your services through deployment, monitoring, andon-call

• Drive frontend architecture decisions, establishing development standards, and creating reusable component libraries.

Contribute to Search Infrastructure

• Work alongside the Search Architect and ML Architect to implement hybrid retrieval systems combining keyword search, dense vector similarity, and reranking.

• Build and maintain Elasticsearch indexing pipelines, query services, and relevance tuning tooling.

• Integrate vector databases (Pinecone,Weaviate, FAISS or similar) into retrieval workflows—even if this is new territory, you'll learn fast.

• Instrument search pipelines with meaningful metrics: CTR, zero-result rate, latency—feeding the team's A/B experimentation loop.

• Build clean, responsive, and production-ready interfaces from wireframes or Figma designs.

Own the Engineering Bar

• Champion CI/CD, observability, testing, and infrastructure-as-code as non-negotiables, not afterthoughts.

• Lead design sessions with Engineers and Architects; translate product requirements into clean, maintainable technical solutions.

• Participate in code reviews and knowledge-sharing—actively raising the team's collective skill level.

WHAT YOU BRING

Must-Haves

• Strong Pythonand Reactfoundation:6+ years of professional backend or full-stack engineering experience with a deep Python/React (e.g.,Next.js, Vite.js, Remix.js, Gatsby.js)focus—async patterns, type annotations, testing, and production-grade service/componentdesign.

• Cloud-native experience:Proven experience designing and deploying cloud-native applications (

GCP strongly preferred; AWS or Azure considered).

• Hands-on experience building resilienthigh throughputmicroservices and RESTful/gRPCAPIs.

• Solid understanding of containerization (Docker), orchestration (Kubernetes), and serverless paradigms.

• Strong grounding in SOLID design principles and software craftsmanship.

• Good communicator who thrives in cross-functional, agile teams alongside ML engineers, architects, and product owners.

• Comfort using AI tools to accelerate development throughput.

• Strong experience of using and managingMonorepos

• Strong understanding of relational (e.g.,PostgreSQL, MySQL, Oracle) and non-relational databases (e.g., MongoDB, DynamoDB).

• Mentorship:Provide guidance and technical knowledge sharing to mid-level and junior developers. 

Strongly Preferred — Search & ML

You don't need all of these on day one—but the more you bring, the faster you'll contribute:

• Search systems:Experience with search platforms: 

Elasticsearch, OpenSearch,Solr, orAlgolia—index management, query DSL, relevance tuning.

• Vector search:Familiarity with vector search concepts and tooling: 

embeddings, approximate nearest neighbor (ANN), FAISS, Pinecone,Weaviate, or similar.

• Exposure to ML/AI patterns: RAG pipelines, LLM integration, prompt engineering, or fine-tuning workflows.

• Experience with AI orchestration frameworks such asLangChain,LangGraph, or Google ADK.

• Infrastructure-as-code experience (Terraform,Pulumi,OpenTofu) and mature CI/CD pipeline ownership.

WHO YOU ARE