Jobs at Mrsool

The Site Reliability Engineer will design scalable infrastructure, automated deployment pipelines, monitoring, and alerting systems. Responsibilities include troubleshooting production incidents, participating in on-call rotations, maintaining systems, improving security and compliance, optimizing reliability and efficiency, and mentoring junior engineers. The role requires collaboration with development and cross-functional teams, technical communication, infrastructure automation, and expertise in distributed systems, cloud platforms, and networking.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Lead end-to-end backend project execution, architecture, and technical decisions for highly concurrent, low-latency systems. Build scalable services using Ruby on Rails and Go, optimize PostgreSQL and Redis performance, maintain data consistency across microservices, and improve observability, testing, and CI/CD. Collaborate cross-functionally, mentor engineers, resolve infrastructure bottlenecks, and guide long-term architectural and organizational improvements.

Build and deploy machine learning and optimization models for marketplace matching, pricing, recommendations, ETA prediction, and conversational ordering. Design experiments and causal analyses, engineer features from large-scale operational data, and own production ML pipelines through deployment, monitoring, and retraining. Collaborate with product, engineering, DevOps, and operations teams to improve marketplace performance and reliability.

Analyze business and operational performance across food delivery, services, and logistics. The role involves querying and validating data, investigating performance trends and root causes, modeling unit economics and profitability, evaluating pricing and promotional impacts, conducting cohort and retention analysis, and translating findings into actionable recommendations for stakeholders.

Lead an 8-10 engineer team owning courier systems (dispatch, matching, pricing, ETA). Set technical direction, drive data-driven decisions and experiments, partner with data science, ensure reliability and scalability of low-latency distributed systems, and own hiring, performance, and operational excellence.