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 model training, model evaluation, and data processing. 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. \n 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 Work Authorization Requirement: This is a remote role open to candidates who are currently authorized to work either in the United States or in Canada without the need for current or future employment-based visa sponsorship. Artera does not sponsor visas for this position. Eligible candidates may include: Individuals authorized to work in the United States on a permanent basis (e.g., U.S. citizens, U.S. permanent residents), or Individuals authorized to work in Canada (e.g., Canadian citizens or Canadian permanent residents). Visa Transfers (if needed). Here are few posts from our teammates, partners and customer voices to highlight the work we do: Artera Shapes the Future of Cancer Treatment Using Machine Learning on AWS How Artera AI test allowed Bruno to avoid hormone therapy Startups are using AI to predict responses to Cancer Drugs ArteraAI validates its Prognostic Model in Advanced Prostate Cancer How Artera Enhances Prostate Cancer Diagnostics Using AWS \n$140,000 - $180,000 a year In addition to base salary, equity is a core component of our compensation. We also offer 401k matching, unlimited paid time off (PTO), and more. The base salary is competitive and commensurate with experience, qualifications, and other factors to be discussed during the interview process. \n#LI-JD1 Equal Employee Opportunity: At Artera, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients and physicians. As an equal opportunity employer, we do not discriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information gender identity or expression, sexual orientation, marital status, protected veteran status, or any other legally protected characteristic.