Top Tech Jobs & Startup Jobs

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.