Jobs at Truecaller

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.

Develop and deliver Android application features and performance improvements across the product lifecycle. Write high-quality, tested code, participate in design and code reviews, debug issues, collaborate with product and release teams, and mentor junior engineers.

Lead quantitative insights for a product team: understand data, define and analyze experiments, build KPIs, create pipelines and dashboards, and mentor colleagues to drive data-driven product decisions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.