SDE IV (ML Engineering)

Job description

InMobi Advertising is a global technology leader helping marketers win the moments that matter. Our advertising platform reaches over 2 billion people across 150+ countries and turns real-time context into business outcomes, delivering results grounded in privacy-first principles. Trusted by 30,000+ brands and leading publishers, InMobi is where intelligence, creativity, and accountability converge. By combining lock screens, apps, TVs, and the open web with AI and machine learning, we deliver receptive attention, precise personalization, and measurable impact. Through Glance AI, we are shaping AI Commerce, reimagining the future of e-commerce with inspiration-led discovery and shopping. Designed to seamlessly integrate into everyday consumer technology, Glance AI transforms every screen into a gateway for instant, personal, and joyful discovery.  Spanning diverse categories such as fashion, beauty, travel, accessories, home décor, pets, and beyond, Glance AI delivers deeply personalized shopping experiences. With rich first-party data and unparalleled consumer access, it harnesses InMobi’s global scale, insights, and targeting capabilities to create high impact, performance driven shopping journeys for brands worldwide. Recognized as a Great Place to Work, and by MIT Technology Review, Fast Company’s Top 10 Innovators, and more, InMobi is a workplace where bold ideas create global impact. Backed by investors including SoftBank, Kleiner Perkins, and Sherpalo Ventures, InMobi has offices across San Mateo, New York, London, Singapore, Tokyo, Seoul, Jakarta, Bengaluru and beyond. AtInMobi Advertising, you’ll have the opportunity to shape how billions of users connect with content, commerce, and brands worldwide. To learn more, visit www.inmobi.com SDE III/IV -  Data Platform Engineering                                     Mission                                                                        Architect a self-serve Data "Platform-as-a-Product" powering InMobi's global-scale data ecosystem. Integrate OSS tools, proprietary services, and  Cloud/SaaS into unified infrastructure. Requires deep data engineering         expertise (batch/streaming pipelines, data modeling, query optimization, governance) combined with platform engineering to build production-grade  solutions for data engineers and analysts.                                                                                                    Core Responsibilities                                                                                                          Design & Development - Bridge OSS tools (Spark, Flink, Airflow, Iceberg), internal services, and cloud offerings into cohesive data platform infrastructure. Build intuitive platform integrations enabling push-button  data workflows.                                                                Scale Engineering - Operate distributed systems processing petabytes of data daily. Own multi-region Kubernetes infrastructure with elastic scalability  and fault tolerance.                                                           Performance Optimization - Optimize compute utilization (Spark/Flink clusters, Velox/Gluten acceleration) for large-scale batch and real-time  streaming with sub-second latency.                                             Observability & Data Quality - Build comprehensive telemetry (metrics, logs,traces) and data quality frameworks for 24/7 uptime. Enforce SLAs/SLOs with automated incident response and data validation.                                                                                                                                                          Required Skills & Experience (Must-Have)                                                                                                                   • 7–10 years building, optimizing, and operating production data platforms     • Deep data engineering fundamentals: data modeling, partitioning strategies, query optimization                                                             • Distributed compute: Spark (PySpark/Scala), Flink streaming, performance  tuning at petabyte scale                                                       • Data lake architecture: Iceberg table format, Polaris catalog, schema   evolution, time travel                                                         • Orchestration: Airflow DAG development, dependency management, SLA  monitoring                                                                     • Data transformation: DBT modeling, testing, documentation, incremental  builds                                                                         • Data quality: Great Expectations, dqueue validation frameworks, drift  detection                                                                      • Query acceleration: Velox, Gluten integration, columnar formats (Parquet, ORC)                                                                           • Data governance: OpenMetadata catalog, lineage tracking, access control      • Kubernetes platform development: operators (Spark/Flink), Yunikorn    scheduler, multi-tenancy, autoscaling                                          • Cloud infrastructure: GKE multi-region clusters, GCS object storage,    hybrid cloud/on-prem architecture                                              • Programming: Python, PySpark, Scala for data pipelines and platform  tooling                                                                        • IaC: Terraform, Helm, GitOps for reproducible deployments                    • CI/CD: Automated testing, deployment pipelines for data platform  components                                                                                                                                           Good-to-Have