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Urgent! AI-Driven Big Data Engineer (PhD Required) Job Opening In Singapore, Singapore – Now Hiring Pixalate, Inc.

AI Driven Big Data Engineer (PhD Required)



Job description

AI- Driven Big Data Engineer Employment Type: Full-Time
Location: Remote, Singapore
Level: 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 (https://gizmodo.com/apple-icloud-private-relay-ad-fraud-scam-research-1849803510)

+ Washington Post: Your kids' apps are spying on them (https://www.washingtonpost.com/technology/2022/06/09/apps-kids-privacy/)

+ ProPublica: Porn, Piracy, Fraud: What Lurks Inside Google's Black Box Ad Empire (https://www.propublica.org/article/google-display-ads-piracy-porn-fraud)

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.



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