Sr AI/ML Engineer

As a Senior AI/ML Engineer in our AdTech team, you will be a hands-on individual contributor building and deploying machine learning models and AI-driven features for our advertising platform. You will partner with engineering, product, and data science to deliver production-grade ML for campaign optimization, user personalization, and creative intelligence—operating at large scale and low latency across billions of ad events per day. You will contribute to modern ML/LLM capabilities and agentic workflows that automate and enhance campaign operations, with a strong focus on reliability, performance, and measurable business impact.

Key Responsibilities

• Machine Learning Delivery: Design, implement, and ship scalable ML solutions for core AdTech use cases (targeting, ranking, pacing, measurement). Own features end-to-end from experimentation to production rollout.
• ML System Design: Build and evolve the ML lifecycle—data preparation, training, evaluation, and real-time inference—ensuring models integrate cleanly with ad serving systems and meet low-latency, high-throughput requirements.
• Technical Contribution: Contribute to the AI/ML technical roadmap by evaluating tools and techniques (including deep learning, LLMs, and retrieval/feature systems). Make pragmatic trade-offs with an eye toward maintainability and operational excellence.
• AI & Agentic Applications: Develop and integrate LLM-powered features and agentic workflows that assist with campaign workflows such as audience insights, bid/budget recommendations, and creative generation—within well-defined guardrails.
• Cross-Functional Collaboration: Work closely with engineering, product, and data science partners to translate marketing objectives into ML-driven solutions, define success metrics, and deliver iterative improvements.
• Performance & Reliability: Operate ML services in a high-concurrency, latency-sensitive environment. Optimize inference paths, implement monitoring/alerting, manage model drift, and ensure safe deployment practices (A/B testing, canaries, rollbacks).
• Mentorship & Best Practices: Mentor and support other engineers through code/model reviews and knowledge sharing. Champion engineering rigor in testing, observability, documentation, and reproducible ML workflows.

Required Qualifications

• 5+ years of experience in software engineering and/or applied machine learning, with a track record of shipping ML systems to production.
• Experience designing and building high-throughput, low-latency services or data pipelines for large-scale applications.
• Working knowledge of the programmatic advertising ecosystem (DSP/SSP/RTB) is a plus; strong adjacent experience (recommendation, ranking, marketplaces) is also valued.
• Proficiency in at least one of Java, Go, or Python for backend services and ML tooling.
• Hands-on experience with ML frameworks (PyTorch or TensorFlow) and common modeling approaches (classification, ranking, embeddings, deep learning).
• Experience with big data and streaming frameworks (e.g., Spark, Kafka) for processing and analyzing large datasets.
• Experience deploying ML to cloud environments (preferably AWS) and operating services at scale.
• Familiarity with data stores (SQL/NoSQL) such as PostgreSQL/MySQL, Cassandra/DynamoDB, Redis, etc.
• Familiarity with containerization/orchestration (Docker, Kubernetes) and CI/CD practices.
• Strong communication skills and ability to collaborate across disciplines; comfort explaining technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with Large Language Models (LLMs) and generative AI applied to advertising (e.g., ad copy generation, creative optimization, or personalized messaging).
• Experience designing and implementing agentic workflows that support autonomous or semi-autonomous decision-making with strong safety/observability guardrails.
• Experience with model serving and optimization for real-time inference (latency-critical environments).
• Familiarity with modern data lake and table formats (e.g., Apache Iceberg, Apache Hudi) for managing large-scale analytical datasets.
• Knowledge of microservices architecture and event-driven design patter