AI-Driven Big Data Engineer

AI- Driven Big Data Engineer Employment Type: Full-TimeLocation : Remote, SingaporeLevel: Entry to Mid Level (PhD Required) Bridge Cutting-Edge AI Research with Petabyte-Scale Data Systems Pixalate is an online trust and safety platform that protects businesses, consumers and children from deceptive, fraudulent and non-compliant mobile, CTV apps and websites. We're seeking a PhD-level Big Data Engineer to revolutionize how AI transforms massive-scale data operations. Our impact is real and measurable. Our software has uncovered: Gizmodo: An iCloud Feature Is Enabling a $65 Million Scam Washington Post: Your kids' apps are spying on them ProPublica: Porn, Piracy, Fraud: What Lurks Inside Google's Black Box Ad Empire About the Role Work at the intersection of big data and AI, where you'll develop intelligent, self-healing data systems processing trillions of data points daily. You'll have autonomy to pursue research in distributed ML systems and AI-enhanced data optimization, with your innovations deployed at unprecedented scale within months, not years. This isn't traditional data engineering - you'll implement agentic AI for autonomous pipeline management, leverage LLMs for data quality assurance, and create ML-optimized architectures that redefine what's possible at petabyte scale. Key Research Areas & Responsibilities AI-Enhanced Data Infrastructure Design intelligent pipelines with autonomous optimization and self-healing capabilities using agentic AI Implement ML-driven anomaly detection for terabyte-scale datasets Distributed Machine Learning at Scale Build distributed ML pipelines Develop real-time feature stores for billions of transactions Optimize feature engineering with AutoML and neural architecture search Required Qualifications Education & Research PhD in Computer Science, Data Science, or Distributed Systems (exceptional Master's with research experience considered) Published research or expertise in distributed computing, ML infrastructure, or stream processing Technical Expertise Core Languages : Expert SQL (window functions, CTEs), Python (Pandas, Polars, PyArrow), Scala/Java Big Data Stack : Spark 3.5+, Flink, Kafka, Ray, Dask Storage & Orchestration : Delta Lake, Iceberg, Airflow, Dagster, Temporal Cloud Platforms : GCP (BigQuery, Dataflow, Vertex AI), AWS (EMR, SageMaker), Azure (Databricks) ML Systems : MLflow, Kubeflow, Feature Stores, Vector Databases, scikit-learn + search CV, H2O AutoML, auto-sklearn, GCP Vertex AI AutoML Tables Neural Architecture Search: KerasTuner, AutoKeras, Ray Tune, Optuna, PyTorch Lightning + Hydra Research Skills Track record with 100TB+ datasets Experience with lakehouse architectures, streaming ML, and graph processing at scale Understanding of distributed systems theory and ML algorithm implementation Preferred Qualifications Experience applying LLMs to data engineering challenges Ability to translate complex AutoML/NAS research into practical production workflows Hands-on project examples of feature engineering automation or NAS experiments Proven success in automating ML pipelines, from raw data to an optimized model architecture Contributions to Apache projects (Spark, Flink, Kafka) Knowledge of privacy-preserving techniques and data mesh architectures What Makes This Role Unique You'll work with one of the few truly petabyte-scale production datasets outside of major tech companies, with the freedom to experiment with cutting-edge approaches. Unlike traditional big data roles, you'll apply the latest AI research to fundamental data challenges - from using LLMs to understand data quality issues to implementing agentic systems that autonomously optimize and heal data pipelines. #J-18808-Ljbffr