Full-Time Machine Learning Engineer

About Us: Artera is an AI startup that develops medical artificial intelligence tests to personalize therapy for cancer patients. Artera is on a mission to personalize medical decisions for patients and physicians on a global scale.

As a Machine Learning Engineer at Artera, you’ll work on the AI Platform team with a focus on establishing scalable and efficient pipelines for data processing and model training. You’ll work closely with AI model developers, fellow machine learning engineers, and our platform engineering team. You’ll ensure that Artera’s model developers can rely on highly efficient, large-scale training regimes and deploy optimized models to production environments.

Essential Responsibilities:

• Build and own tools and libraries that accelerate Artera’s ability 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 process 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:

• 4+ years of industry software engineering experience
• 3+ years of industry experience using one of PyTorch, TensorFlow, or JAX in Python
• 2+ 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 using Terraform, SqlAlchemy
• Experience using ML orchestration frameworks such as Ray, Kubeflow, Metaflow, MLFlow, Flyte, Dagster, Argo Workflow or Prefect
• 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

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