StaffMachineLearningEngineer,MLInfrastructure

The opportunity Unity Vector builds ML infrastructure that powers real-time prediction, experimentation, attribution, and AI-driven decision-making across the company. Our online ML systems serve production models at scale, supporting low-latency inference, large-scale experimentation, model deployment and optimization, feature processing, and business-critical decisioning. As model complexity, traffic volume, and experimentation velocity continue to grow, our inference platform must remain reliable, scalable, observable, and cost-efficient. To support this growth, we need strong technical ownership to evolve the online ML infrastructure that enables ML teams to safely deploy, validate, and operate production models at scale. The Role We are seeking a senior/staff ML engineer to design and evolve Unity Vector’s online model inference platform. This role focuses on building reliable infrastructure for serving machine learning models in production, optimizing inference performance, and enabling safe, efficient experimentation across high-traffic online systems. You will work closely with ML engineers, platform teams, and product stakeholders to ensure models can be deployed, scaled, monitored, and iterated on efficiently. You will play a key role in shaping how models are packaged, served, validated, monitored, and optimized in production environments. This role requires strong systems thinking, deep experience with production ML infrastructure, and the ability to drive architectural improvements across teams. What you'll be doing Design and operate large-scale online inference infrastructure that serves production ML models with low latency and high reliability. Build and improve model serving systems using technologies such as PyTorch, Triton Inference Server, Kubernetes, GKE, Ray, or similar distributed serving frameworks. Optimize inference performance through batching, model compilation, GPU/CPU utilization improvements, request scheduling, and runtime-level tuni