Jobs at Truecaller

As a Senior Site Reliability Engineer, you will manage and maintain infrastructure services, improve system performance, and ensure reliability and availability.

Lead quantitative insights and experimentation for product teams: define and analyze experiments, build KPIs, create data pipelines and dashboards, mentor teammates, and collaborate with product, tech, and design to drive data-informed decisions.

Provide end-user IT support and system administration across Windows, macOS, and Linux. Manage onboarding, account provisioning, network and device troubleshooting, inventory, license procurement, vendor relationships, ISO-aligned security and compliance, documentation, and process improvements to maintain reliable IT operations.

Lead design and build of scalable, low-latency data pipelines and core data infrastructure (Spark, Kafka, BigQuery, Airflow). Operationalize ML models, drive cross-squad delivery, improve system reliability, and mentor engineers while owning large ambiguous problems end-to-end.

Own the supply-side product roadmap for Truecallers ads: unlock and evolve inventory, improve ad experience quality (viewability, latency, click quality), build signal and measurement foundations, ensure compliance, and collaborate with demand, engineering, design, and ad operations to drive monetization without degrading user experience.

Own the intelligence layer of Truecallers programmatic ads platform: define strategy and roadmap for decisioning, pricing, and auction systems; build auction intelligence and pricing strategies; drive yield (eCPM, fill, win rate); optimize performance, reliability, and latency at scale; design long-term platform capabilities; set KPIs and run experiments to iterate and measure impact.

Build, test, and deploy high-traffic backend services; own project delivery and quality; ensure stability, performance, and maintainability; collaborate across functions from design to production and improve team processes.

Lead and grow a multidisciplinary ML and data engineering team building retrieval, ranking, contextual-bandit systems and real-time serving/data platforms. Set technical direction, prioritize delivery, ensure reliability and experimentation rigor, partner with product and cross-functional teams, and manage hiring, performance, and career development.

Design, build, and maintain scalable data pipelines and infrastructure powering SMS features, fraud detection, and ML capabilities. Collaborate with data scientists, analysts, and product teams to enable feature engineering, ensure reliable ingest of billions of events daily, improve platform tooling, and establish best practices for production data workflows.

Build and maintain scalable data ingestion and ETL pipelines delivering billions of events daily; support and optimize teams' pipelines; develop platform tools and frameworks; collaborate with data scientists and analysts; implement best practices for software and data development; work on streaming and ML platform projects.

Build and own large-scale feature and training-data pipelines, streaming ingestion, real-time feature store, and ad-event attribution backbones supporting recommendation and ads ML systems. Collaborate with data scientists to productionize models, ensure data freshness and lineage, and maintain reliable, monitored pipelines at scale.

Design, build, and maintain core Android platform components (networking, architecture, performance, shared libraries). Deliver features, write tests, participate in design reviews, mentor junior developers, and collaborate with release teams to ensure high-quality continuous delivery.

Optimize training and deployment of ML models for cost, speed, and mobile use. Enable teams to deploy models to production, specialize in on-device ML (Android/iOS), drive best practices, and explore federated learning and new ML deployment techniques.

Lead and grow a multidisciplinary ML and data engineering team building recommendation and ad-serving systems. Set technical direction across retrieval, ranking, contextual bandits, and real-time serving. Own planning, delivery, experimentation rigor, production reliability at scale, and people development while partnering with product and cross-functional stakeholders.

Lead and grow a Data Science team for Truecaller for Business, define strategy and KPIs, architect and deploy scalable ML models, ensure model governance and MLOps best practices, partner with product and engineering, and drive measurable business impact.

Lead data science initiatives across product and engineering: design ML solutions, gather and analyze data, build and productionize data products, collaborate with stakeholders, and mentor other data scientists to improve technical skills and delivery.

Design, build, and operate scalable, low-latency backend services for a high-volume distributed system. Ensure high availability, collaborate with cross-functional teams, mentor engineers, evaluate new technologies, and drive delivery of reliable systems supporting millions of users.

Design, build, and deploy ML models for fraud, risk, and intelligence products; translate business problems into data solutions; develop anomaly detection and graph-based methods; own end-to-end model development, deployment, and monitoring; run experiments and manage large multi-country datasets; partner cross-functionally to ship privacy-safe data products and explain model outputs to technical and non-technical stakeholders.

Design, develop, and maintain Truecaller9s iOS application across features from authentication to search. Collaborate with cross-functional teams, ensure app performance and responsiveness, debug complex issues, participate in design and code reviews, follow SDLC, and write clean, maintainable code.

Lead multi-team backend initiatives, provide hands-on technical leadership, mentor engineers, influence product scope, ensure scalable, highly available distributed systems, manage databases and security best practices, align cross-functional stakeholders, and participate in hiring.