SDE IV (ML Engineering)
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What you'll do
- 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.
What they're looking for
- 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
Nice to have
- Experience building cloud data platform / control plane development
- Advanced observability: Prometheus/Grafana, Loki, Firehydrant integration
- Real-time streaming: Kafka integration, exactly-once semantics, backpressure handling
- Cost optimization: Resource allocation, query optimization, storage tiering strategies
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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.
At InMobi 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
What you'll do
- Turn the Data Scientist's findings into production systems: finetuning/distillation pipelines, routing algorithms, cost-optimization logic
- Build data pipelines and platform components as owned products with real interfaces, not one-off scripts
- Own the engineering rigor of shared evaluation infrastructure so both model work and algorithm work rely on the same trusted foundation
- Ship, monitor, and iterate on what's in production, closing the loop back to the Data Scientist on what's working
What we're looking for
- Strong software/ML engineering fundamentals — production code quality, comfort building and shipping pipelines and services
- Genuine willingness to learn model finetuning and distillation on the job — prior experience is a plus, not a requirement
- Algorithmic thinking for routing/optimization problems under cost, latency, and non-determinism constraints
- Comfortable working from someone else's analysis rather than always originating the “what should we build” question
The InMobi Culture
At InMobi, culture isn’t a buzzword; it's an ethos woven by every InMobian, reflecting our diverse backgrounds and experiences.
We thrive on challenges and seize every opportunity for growth. Our core values — thinking big, being passionate, showing accountability, and taking ownership with freedom — guide us in every decision we make.
We believe in nurturing and investing in your development through continuous learning and career progression with our InMobi Live Your Potential program.
InMobi is proud to be an Equal Employment Opportunity employer and is committed to providing reasonable accommodations to qualified individuals with disabilities throughout the hiring process and in the workplace.
Visit https://www.inmobi.com/company/careers to better understand our benefits, values, and more!
Company
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