• Design, implement, and optimize FPGA logic using AMD/Xilinx toolchains (Vivado, Vitis, HLS) development in VHDL/Verilog • Integrate FPGA designs into larger systems, ensuring robust verification, documentation, and deployment across multiple platforms (Zynq, UltraScale+, Versal) • Develop innovative machine learning and computer vision solutions to analyze and exploit large, complex datasets from remote sensing phenomenology • Develop algorithms and associated software tools using C/C++/Python and associated machine learning libraries (PyTorch, LibTorch) • Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives • Provide solutions for data collection and data linting that enable rapid, automated curation of training data • Keep up with the SoTA practices for AI/ML • Adhere to teams’ standards for reviewing source code, unit-testing, source code control, and documentation practices • Utilize Python PEP8 standards.

• Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges. • Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerization practices. • Build high-performance data pipelines (ETL/ELT) for both training and real-time inference. • Act as a technical lead for the team, mentoring junior engineers and setting engineering best practices. • Partner with Product Managers and Data Scientists to translate business ambitions into sophisticated technical requirements.

• Build a next-generation ML data and feature platform to significantly improve productivity of ML practitioners • Collaborate closely with ML practitioners and domain experts to ensure high-quality features and labels • Design and build a near-real-time feature computation engine for generating ML features • Operate and manage feature computation pipelines and serving infrastructure for various ML models • Build and scale systems that accelerate training through performant data loading, transformation, and writing • Develop feature stores that enable feature discovery and sharing

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description 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. - Develop the long term vision and roadmap for Artera’s AI platform that will allow the company to continue to scale in terms of both increased inference volume and development workloads. - 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. Qualifications - 8+ years of industry software engineering experience - 4+ years of industry experience in using ML orchestration frameworks such as Flyte, Ray, Kubeflow, Metaflow, MLFlow, Dagster, Argo Workflow or Prefect - 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 Requirements - 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 Benefits - $180,000 - $220,000 a year - Equity is a core component of our compensation. - 401k matching - Unlimited paid time off (PTO) - The base salary is competitive and commensurate with experience, qualifications, and other factors to be discussed during the interview process. 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. - 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). 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.