Location - Menlo Park, CA ( Onsite DAY1 ) 4 days WFO

AI Engineer with 6–10 years of experience designing and deploying scalable AI/ML solutions for AdTech platforms covering targeting, bidding, personalization, attribution, and real-time analytics.

The role requires strong engineering fundamentals with hands-on ML model development, data pipelines, and real-time decision systems, leveraging modern distributed and cloud-based architectures.

Key Responsibilities

Develop and deploy AI/ML models for:
Audience targeting & segmentation
Ad ranking & bidding optimization
Attribution & campaign performance modelling
Fraud detection & anomaly detection
Build and optimize end-to-end ML pipelines:
Data ingestion, feature engineering, training, and inference
Batch & real-time model serving
Design real-time decisioning systems for high-throughput, low-latency environments.
Collaborate with data engineers and architects to ensure:
Scalable data pipelines (ETL/ELT, streaming)
High-quality feature stores and model lifecycle management
Drive experimentation frameworks (A/B testing, causal inference) to continuously optimize performance metrics.
Ensure privacy-aware and compliant AI solutions aligned with data governance frameworks.

Required Qualifications

Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or related field.
6–10 years of experience in AI/ML engineering / Data Science engineering roles.
Strong programming skills in:
Python (mandatory)
Java or C++ (preferred)
Hands-on experience in:
ML frameworks (TensorFlow, PyTorch, XGBoost)
Distributed processing (Spark, Flink)
Streaming systems (Kafka)
SQL & NoSQL databases
Experience building production-grade ML pipelines and scalable data systems

Preferred Qualifications

Experience in AdTech / MarTech / Retail Media ecosystems
Exposure to:
Recommendation systems
Real-time bidding systems
Experimentation platforms / A/B testing
Familiarity with:
Kubernetes, Docker, microservices
Privacy and regulatory frameworks (GDPR, data compliance)