Senior Machine Learning Engineer, Real-Time Bidding

About the Role

Trellis is hiring a Senior Machine Learning Engineer to advance our real-time bidding (RTB) systems. You will design, implement, and scale ML models that directly power high-volume bidding decisions across millions of daily ad auctions. This includes end-to-end ownership of the ML lifecycle: data exploration, feature engineering, model training, deployment, and monitoring in production. You’ll leverage Google Cloud Platform services (BigQuery, Vertex AI, Dataflow, Pub/Sub) and Python-based ML frameworks (TensorFlow, PyTorch, Scikit-learn) to deliver measurable improvements in bidding efficiency and ROI.

This is a fully remote position, based in the US or Canada, and will report directly into the Head of RTB, Thomas Boquet.

What You’ll Do

• Research, design, and implement advanced ML models for conversion rate prediction, value estimation, and pacing.
• Build robust feature pipelines with BigQuery and Dataflow, ensuring low-latency inference at scale.
• Deploy, monitor, and continuously improve models in production using Vertex AI.
• Develop and maintain MLOps frameworks to automate training, evaluation, and deployment.
• Partner with backend and distributed systems engineers to integrate models into high-throughput bidding services.
• Contribute to experimentation frameworks and AB testing of ML-driven bidding strategies.

What You’ll Need

• 5+ years of ML engineering experience, including end-to-end deployment in production environments.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, or Scikit-learn).
• Experience with cloud ML platforms, preferably GCP (Vertex AI, Dataflow, Pub/Sub, BigQuery).
• Familiarity with MLOps practices (CI/CD for ML, monitoring, reproducibility).
• Strong data skills: SQL, feature engineering, and big data processing.
• A pragmatic mindset: delivering fast, measurable improvements while balancing complexity and scale.