Our client, a recognized leader in data solutions focused on improving transportation, is seeking an accomplished Principal Machine Learning Engineer to join their Advertising Data Science team. This is a high-impact role for an expert capable of applying cutting-edge Deep Learning, Reinforcement Learning (RL), and advanced Machine Learning techniques to complex, large-scale systems within a dynamic AdTech environment.

If you are driven by the challenge of designing, customizing, and deploying sophisticated algorithmic solutions that directly impact business KPIs, this opportunity is for you.

The Opportunity: Algorithmic Innovation in AdTech

You will be a key contributor on a team responsible for the intelligence and efficiency of our clients' in-house Marketing Platform. This platform leverages proprietary driving data (e.g., GPS, driving events) combined with traditional Ad Platform data (impressions, clicks, conversions) to empower marketers with highly effective and relevant messaging.

The core of this role is not routine coding; it’s about advanced model development, customization, and algorithmic design. You will take ownership of the entire optimization cycle—from research and prototyping to productionization and A/B testing—working closely with product and engineering partners.

Key Areas of Focus:

Reinforcement Learning (RL): Design and implement complex RL frameworks, applying algorithms such as Policy Gradient or Q-Learning to solve challenging system-level problems (e.g., dynamic bidding, pacing control). You will be expected to customize and adapt existing packages to precisely meet the optimization goals.
Deep Learning (DL): Lead the development and deployment of bespoke neural network architectures, moving beyond standard libraries. This involves designing specific layer structures, choosing appropriate tools (e.g., PyTorch, TensorFlow), and developing novel models for predictive tasks like Click-Through Rate (CTR) and Conversion Prediction.
Large Language Models (LLMs): Explore and apply advanced LLM-type solutions as they become relevant to enhancing platform capabilities and personalization.
Platform Optimization: Develop, iterate, and deploy models that enhance platform efficiency, including Win Rate Modeling, Frequency Capping, and Platform Simulation. A background in AdTech (real-time bidding, bid shading, ads ranking) is a strong advantage.

Core Responsibilities:

Serve as a thought partner, identifying and autonomously pursuing advanced research that delivers significant business impact on KPIs across the Ads Platform.
Design, build, and deploy advanced Machine Learning and Deep Learning models on large-scale datasets, leveraging distributed computing frameworks (e.g., Spark).
Focus on model customization and optimization, going beyond standard package implementation to modify algorithms and tailor models to specific, complex business requirements.
Establish and champion data science best practices, including robust peer/code review processes, strong documentation, and rigorous standards for reproducibility.
Collaborate with Product Owners and Software Engineers to seamlessly transition research prototypes into scalable, measurable, and production-ready solutions.

Qualifications

Minimum Requirements:

Master’s degree with 7+ years or a Bachelor’s degree with 10+ years of industry experience as a Data Scientist or Machine Learning Engineer.
Deep and proven expertise in building and deploying customized, advanced Machine Learning and Deep Learning models, focusing on model development, architecture design, and lifecycle management. Demonstrable experience with Reinforcement Learning algorithms such as Qlearning, Multi-armed Bandits, or Deep Reinforcement Learning, including experience in designing system-level solutions.
Strong programming proficiency in Python and Spark (or equivalent distributed computing tools) for production-level engineering and large-scale data processing.
Expertise with scientific computing and ML libraries such as TensorFlow, PyTorch, and Spark MLlib.

Preferred Qualifications:

Hands-on experience developing advanced data science solutions within the digital advertising domain (e.g., real-time bidding, ads ranking, bid shading, or CTR/conversion prediction).
Knowledge of Control Theory and its application to platform optimization.
Experience with cloud AI/ML platforms (e.g., Vertex AI, Sagemaker) for model deployment.

This role is for a hands-on technical expert who thrives on complexity and the opportunity to make highly technical, data-driven decisions that shape a leading-edge advertising platform. If you have a track record of applying state-of-the-art model development to solve complex, system-level challenges, we encourage you to apply.