Senior ML Backend Engineer – Search & Recommendations

Salla E-Commerce Platform

. Build and run query and recommendation serving: retrieval, score fusion, filtering, and caching .

Posted 10/15/2026full-timeRemote • Saudi ArabiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining low-latency backend systems, particularly in production search engines, with a strong focus on query and recommendation serving, data ingestion, and system monitoring. Proficient in Python and TypeScript, with experience in A/B testing and change-data-capture methodologies.

Highest-signal resume keywords

Production Search Engine ExperienceLow-Latency ServicesStrong Python ProficiencyTypeScript DevelopmentStreaming Ingestion (Kafka, Debezium)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend System DevelopmentQuery ServingReindexingChange-Data-CaptureExperimentation (A/B Testing)Feature LoggingMonitoring and AlertingData Freshness ManagementLatency OptimizationDebugging Relevance Issues

Tools & Technologies

ElasticsearchOpenSearchSolrTypesenseVespaKafkaDebeziumClaude Code

Industry Keywords

Search EngineIndex LifecycleScore FusionFilteringCachingShadow TrafficGuardrail MetricsRollbackProduction OwnershipCollaboration with Data Scientists

Tech Stack

Tools & technologies

ElasticSearchKafkaPythonTypeScript

About the role

Key responsibilities & impact

Build and run query and recommendation serving: retrieval, score fusion, filtering, and caching
Maintain double-digit-millisecond p95 latency for search-as-you-type
Design graceful degradation for slow or unavailable models and vector paths
Build experiment plumbing including shadow traffic, A/B assignment, guardrail metrics, and rollback
Build change-data-capture from source systems into search indexes and feature stores with measured freshness
Keep embeddings fresh for new and updated items
Run zero-downtime reindexing of multi-million-document collections
Build feature logging that turns production traffic into training data for ranking models
Ship changes into product teams' backends and clients, including new endpoints, cutovers, and experiment flags
Work with product engineering teams on reviews, release windows, and production ownership
Provide monitoring, alerting, runbooks, and on-call support for shipped systems
Collaborate with search data scientists and the ranking lead on search and recommendation serving

Requirements

What you’ll need

5+ years building backend or platform systems in production
2+ years operating a production search engine (Elasticsearch, OpenSearch, Solr, Typesense, Vespa or similar): index lifecycle, analyzers, reindexing at scale, debugging relevance issues with data scientists
Low-latency services under real traffic, and the ability to explain where the p95 goes
Streaming ingestion: Kafka, Debezium or similar change-data-capture, plus idempotent backfills
Strong Python
Comfortable shipping in TypeScript
Shipped changes through A/B tests and staged rollouts
Daily use of agentic coding tools such as Claude Code