AI/ML Engineer / Data Scientist (AdTech / MarTech / Retail Media)

Title: AI/ML Engineer / Data Scientist – AdTech / MarTech / Retail Media
Location: Menlo Park, CA (Hybrid)

Job Description:

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)