Member of Technical Staff, Machine Learning Infrastructure

Job summary

Responsibilities

As an ML Infrastructure Engineer at Abridge, you’ll play a pivotal role in building and optimizing the core inference infrastructure that powers our machine learning models. Your work will be instrumental in enhancing the scalability, efficiency, and performance of our AI-driven solutions.

• Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
• Develop, optimize, and maintain ML model serving infrastructure, ensuring high-performance and low-latency.
• Collaborate with ML and product teams to scale backend infrastructure for AI-driven products, focusing on model deployment, throughput optimization, and compute efficiency.
• Optimize compute-heavy workflows and enhance GPU utilization for ML workloads.
• Build a robust model API orchestration system

What they're looking for

• 5+ years of experience in building and deploying machine learning models in production environments.
• Deep understanding of container orchestration and distributed systems architecture
• Expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
• Experience developing APIs and managing distributed systems for both batch and real-time workloads
• Excellent communication skills, with the ability to interface between research and product engineering

Tech stack

• Kubernetes
• Triton
• vLLM
• PyTorch
• TensorFlow
• CUDA
• Terraform