Description
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.
