Machine Learning Engineer (Platform)

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.

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

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