Senior ML Backend Engineer - Search & Recommendations

Salla; Medina; Presencial; SAR 200.000 - 360.000; Jornada completa

Descripción de la vacante

Salla, a leading e-commerce platform in the MENA region, seeks an experienced Backend/Platform Engineer to own the online serving and streaming components of its discovery stack. You will work on fast, fresh, and reliable ranking and recommendation systems that power shopper experiences.

You will collaborate with data scientists and ML engineers to deploy scalable search and ranking models, ensuring robust operation, monitoring, and safe deployment through AB testing and staged rollouts.

Formación

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

Responsabilidades

• Online serving: Build and run query and recommendation serving, retrieval, score fusion, filtering, caching.
• Maintain tight latency budgets including double-digit-millisecond p95 for search-as-you-type.
• Design graceful degradation for slow or down model/vector paths.
• Build experiment plumbing and shadow traffic, AB tests, and rollouts.
• Streaming data: 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.
• Integration: Ship changes into product backends and clients, new endpoints, cutovers, experiment flags.
• Collaborate with product engineering on reviews, release windows, and production ownership.
• Operability: Monitoring, alerting, runbooks, and on-call responsibilities for shipped features.

Conocimientos

Backend systems

Production search

Low-latency services

Python

TypeScript

Data-driven ranking

A/B testing

AI tooling awareness

Herramientas

Elasticsearch

OpenSearch

Solr

Kafka

Debezium

Descripción del empleo

You ll own the online systems that take a catalogue change to a ranked result in front of a shopper search retrieval and recommendation serving the streaming data that keeps them fresh and the experiments that ship them Salla is the leading e-commerce platform in MENA serving about 68 000 merchants Our AI team is building a self-owned discovery stack across our marketplace and merchant stores hybrid lexical and vector search Arabic-first query understanding and learned ranking shared between search and recommendations Data scientists own the relevance and ranking models You own the platform that makes them fast fresh reliable and shippable The work is roughly half online serving a third streaming data and the rest integration into product surfaces You sit on the ML Platform team spending time between search and serving for our recommendation systems and work day to day with search data scientists and the ranking lead.

What you ll do

Online serving

• Build and run query and recommendation serving
• retrieval
• score fusion
• filtering
• caching
• Hold tight latency budgets including double-digit-millisecond p95 for search-as-you-type
• Design graceful degradation so serving holds up when a model or vector path is slow or down
• Build experiment plumbing shadow traffic
• A B assignment guardrail metrics rollback

Streaming data

• 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

Integration

• Ship changes into product teams backends and clients new endpoints cutovers experiment flags
• Work with product engineering teams on reviews release windows and production ownership

Operability

• Monitoring
• alerting runbooks
• and your share of on-call for what you ship

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. We expect one engineer plus agents to cover what used to take a team.