Position Overview

As a

Staff Machine Learning Engineer on Trellis’s Real-Time Bidding team , you will build, deploy, and optimize the ML models that drive >$100 million of annual programmatic marketing spend. You will work side-by-side with our Data Engineering team to harness high-quality data, craft robust real-time solutions, and continuously enhance model performance in a low-latency, revenue-critical environment.Who You Are

Analytical & Detail-Oriented:

You have a solid grounding in statistics and machine learning, with a keen eye for detail.Collaborative Communicator:

You excel at working cross-functionally, ensuring technical and business trade-offs are clearly understood.Self-Motivated & Pragmatic:

You thrive in fast-paced environments, managing multiple priorities while delivering practical, scalable solutions.Innovative Problem-Solver:

You’re eager to tackle complex challenges, iterating quickly and learning continuously.What You’ll Do

Own the End-to-End ML Lifecycle:

Design, build, deploy, and improve ML models that power our real-time bidding platform.Continuously monitor, evaluate, and optimize model performance for maximum ROI.

Contribute to Business Strategy:

Work cross-functionally with product and business stakeholders to translate high-level objectives into tangible, ML-driven solutions that maximize ROAS in programmatic auctions.

Apply Statistical & ML Expertise:

Utilize advanced statistical techniques and modern ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) to predict auction outcomes and user behaviors.Incorporate real-time feedback loops to adapt swiftly to shifts in the RTB marketplace.

Drive Team Excellence:

Mentor and guide team members through technical leadership, code reviews, and sharing best practices.Balance urgency with the delivery of robust, scalable solutions in a dynamic startup environment.

Architect Scalable Services:

Leverage Kubernetes and managed services on GCP to deploy and orchestrate low-latency, high-availability services.Implement best-practice observability, logging, and monitoring to ensure system reliability and efficiency.

What You’ll Need

Advanced SQL & Data Handling:

Proficiency with complex queries, performance tuning, and managing large-scale data processing.Experience collaborating with a Data Engineering team to ensure data integrity and efficiency.

Real-Time Bidding / AdTech Knowledge:

Experience with or good understanding of the RTB ecosystem (DSPs, SSPs, auctions, ROI optimization) and designing low-latency systems.

Statistical & Machine Learning Fluency:

Solid foundation in statistics, probability, and modern ML techniques.Proficiency with frameworks like TensorFlow, PyTorch, XGBoost/Catboost, or scikit-learn.

Teamwork, Accountability & Communication:

Demonstrated success working with cross-functional teams, clearly articulating technical and business trade-offs.

Autonomy & Prioritization:

Self-driven and capable of managing multiple priorities while making practical trade-offs in a dynamic startup environment.

Cloud & Kubernetes Expertise:

Proven experience designing, deploying, and maintaining services on GCP or another major cloud platform.Deep hands-on experience with Kubernetes for container orchestration and microservices architecture.

#J-18808-Ljbffr