Principal AI/ML Engineer - AdTech

What you'll do

• Lead the design and implementation of scalable, high-performance, and resilient ML solutions for AdTech use cases.
• Set technical direction for integrating AI/ML into the Demand-Side Platform and broader ad tech stack.
• Architect and evolve the end-to-end machine learning pipeline – from data ingestion and training to real-time inference for real-time bidding, targeting, and optimization algorithms.
• Ensure models seamlessly integrate with ad serving architecture and handle low-latency, high-throughput requirements.
• Define the technical roadmap and vision for AI/ML in the platform, evaluating new tools and techniques including deep learning and LLMs.
• Develop intelligent systems using AI agents and agentic workflows to automate and optimize end-to-end campaign processes.
• Leverage LLMs and generative AI to enable autonomous campaign management tasks such as audience segmentation, dynamic bid adjustments, and creative asset generation.
• Partner with engineering, product, and data science teams to translate marketing objectives into ML-driven solutions.
• Ensure system robustness and stability for ML services in a high-concurrency, low-latency environment.
• Optimize algorithms and infrastructure for speed and scalability, and implement monitoring to maintain model performance and uptime in production.
• Provide technical guidance and mentorship to other engineers and data scientists, fostering a culture of excellence in engineering and ML best practices.
• Review code and models, share knowledge, and champion continuous improvement across teams.

Requirements

• 10+ years of experience in software engineering or data science, with at least 3-5 years in a principal engineer or lead ML role (preferably in the AdTech/MarTech industry).
• Proven experience designing and building high-throughput, low-latency distributed systems or data pipelines for large-scale applications.
• Deep expertise in the programmatic advertising ecosystem, including Demand-Side Platforms (DSPs), real-time bidding (RTB), Supply-Side Platforms (SSPs), and ad exchanges.
• Proficiency in programming languages such as Java, Go, and Python for building both data-intensive backend services and ML tools.
• Hands-on experience with machine learning frameworks and libraries, especially PyTorch or TensorFlow, for developing and training models.
• Strong experience with big data and streaming frameworks (e.g., Apache Spark, Kafka, Hadoop) for processing and analyzing large datasets.
• Expertise with cloud platforms (preferably AWS) and related services for scalable ML model deployment and data storage.
• Experience with various data stores, including both SQL and NoSQL databases (e.g., MySQL/PostgreSQL, Cassandra, DynamoDB, Redis).
• Familiarity with containerization and orchestration technologies (Docker, Kubernetes) for deploying and managing services at scale.
• Excellent communication, presentation, and interpersonal skills, with ability to convey complex ML concepts to technical and non-technical stakeholders.

Tech stack

Benefits