Salla
About Salla
About Salla
Salla is a leading e-commerce platform dedicated to empowering businesses across the Middle East. We provide merchants with the tools and resources they need to build, manage, and grow their online stores, regardless of technical expertise. Our mission is to democratize e-commerce, enabling anyone with a vision to succeed in the digital marketplace.
Our Culture
At Salla, we foster a collaborative and innovative environment where every team member can make a significant impact. We believe in:
- Empowerment: Giving our employees the autonomy to own their work and drive results.
- Innovation: Encouraging creative thinking and continuous improvement.
- Collaboration: Working together as a team to achieve shared goals.
- Customer Focus: Prioritizing the needs of our merchants and delivering exceptional service.
- Growth: Providing opportunities for professional development and advancement.
Why Join Salla?
Joining Salla means becoming part of a dynamic and rapidly growing company that is transforming the e-commerce landscape in the Middle East. You'll have the chance to work on cutting-edge technology, collaborate with talented individuals, and make a real difference in the lives of entrepreneurs. We offer competitive salaries, comprehensive benefits, and a supportive work environment that values your contributions. If you're passionate about e-commerce and driven to succeed, we invite you to explore career opportunities at Salla.
Our Vision
We envision a future where every business, regardless of size, has the opportunity to thrive online. Salla is committed to providing the platform and support necessary to make this vision a reality, driving economic growth and empowering entrepreneurs across the region.
Open Positions
Senior Site Reliability Engineer (SRE)
Salla
Improving Platform Reliability at Salla Leading high-severity incident response and driving post-incident reviews to enhance our platform reliability. Troubleshooting complex issues across applications, infrastructure, and networks. Improving MTTR through better monitoring, alerts, and diagnostic tooling to ensure continuous platform reliability. Participating in the on-call rotation supporting production systems. Performance & Scalability You will be responsible for identifying and resolving performance bottlenecks and scaling challenges. This includes conducting load testing and capacity planning for high-traffic scenarios. Your focus on platform reliability will be essential. Infrastructure & Operations Enhance cloud-native infrastructure, deployment processes, and automation. Improve resilience, fault-tolerance, and recovery mechanisms across systems. This is integral to platform reliability. We also want to ensure proper security so it's important to keep updated with resources from OWASP and similar sites. Observability Build and refine dashboards, alerts, metrics, logs, and traces. Define SLIs/SLOs and improve visibility into system behavior. This ensures complete platform reliability. Tooling & Automation Develop tools that reduce operational toil and increase reliability. Contribute to infrastructure-as-code, CI/CD pipelines, and GitOps workflows which all help to improve platform reliability. Collaboration Work closely with engineering teams to ensure services are robust and production-ready. Mentor engineers on reliability, debugging, and operational best practices. Internal documentation and standardization is something that can aid this, resources like Atlassian can provide insight. Bonus Skills: Background in large-scale, high-traffic systems. Experience with fault-tolerant design, DR, and HA patterns. Familiarity with SLOs, SLIs, and error budgets. Candidates located within GMT 0 to +6 time zones are preferred. We also like to utilize tools like Grafana to improve the development process.
Senior / Staff Data Scientist - recommendations/personalization Systems
Salla
About The Job Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As the Data Science Manager for the Recommendation Systems Pod, you will lead the design and execution of large-scale personalization models that directly impact the company topline. This is a rare opportunity to shape the next generation of commerce AI in a high-growth market characterized by highly diverse user and merchant behaviors across the GCC. Responsibilities Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). Develop multi-task learning approaches that optimize engagement, conversion, and merchant outcomes simultaneously. Build scalable retrieval and ranking systems with ANN search (FAISS, ScaNN) and vector embeddings trained on user, product, and event data. Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). Run A/B tests and interpret results using causal inference and uplift modeling to drive measurable business impact. Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages. Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift). Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies. Mentor junior data scientists and define best practices Qualifications Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field. 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems. Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily. Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning. Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV). Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent. Strong understanding of offline/online evaluation metrics, A/B experimentation, and model monitoring frameworks. Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments.
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