Machine Learning Engineer (AI Platform Lead)

Essential Responsibilities

• Accountable for Artera’s ML compute infrastructure including scaling up Artera’s Foundation Model development by developing distributed training infrastructure and developer libraries.
• Build and evolve the core libraries used by AI scientists to develop, launch, and monitor AI products.
• Work with model developers to optimize GPU and CPU efficiency and data throughput of large-scale foundation models and downstream model training runs.
• Optimize Artera’s ability to store and serve terabytes of digital pathology data efficiently for the use in serving large-scale training regimes.
• Ensure that Artera’s observability infrastructure provides a clear picture of how to continue to optimize performance across our model landscape.

Experience Requirements

• 5+ years of industry software engineering experience
• 4+ years of industry experience using one of PyTorch, TensorFlow, or JAX in Python
• 3+ years of industry experience building with AWS, Docker, and Kubernetes
• 1+ years of industry experience optimizing large-scale, high data-throughput, distributed machine learning training pipelines

Desired

• Experience in using ML orchestration frameworks such as Flyte, Ray, Kubeflow, Metaflow, MLFlow, Dagster, Argo Workflow or Prefect
• Experience using Terraform, SqlAlchemy
• Experience in multi-node and multi-gpu training
• Experience deploying and maintaining infrastructure for machine learning training and production inference
• Familiarity with TorchScript, ONNXRuntime, DeepSpeed, AWS Neuron or similar approaches to inference optimization