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.

• Design, build, and deploy machine learning models in production environments • Develop classification systems to automatically categorize educational content by subject, grade level, and learning standards • Build and maintain computer vision pipelines (e.g., object detection, OCR, image segmentation) for worksheet and student-submission analysis • Design and implement RAG (Retrieval-Augmented Generation) systems using large content libraries • Engineer and optimize prompts for AI-generated educational content and assessment validation • Build AI-driven quality assurance systems to evaluate generated content against educational taxonomies • Develop agentic AI workflows that iteratively refine and improve generated outputs • Deploy and operate AI services on AWS, ensuring scalability, reliability, and cost efficiency • Collaborate directly with client stakeholders and cross-functional engineering teams • Define and, when required, contribute to data labeling or feedback strategies supporting model quality • Maintain clear documentation for models, pipelines, and AI system behavior • Participate in code reviews and promote AI engineering best practices

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a Principal Machine Learning Engineer, a senior technical visionary, to be the Principal Technical Lead for the Growth Engineering team, responsible for setting up overall technical strategy, unified technical architecture and defining a roadmap for industry leading methodology for user and engagement growth. Strong hands-on machine learning background including deep learning architectures, generative AI, and large scale deployment and measurement of ML systems is required. As the Principal Machine Learning Engineer you'll be responsible for the technical direction, strategy and health of our Growth Engineering org. You'll ensure that our technology can deliver on the business/product requirements necessary to keep Pinterest as an engaging platform for everyone. This means working with other leads to set and execute a long-term strategy for the overall engagement growth of Pinterest, aligning the strategy with other internal partners where it makes sense and communicating to leadership our current status and path to having world-class Growth capabilities. You'll also foster a healthy community where all ML engineers can learn best practices, collaborate effectively and understand our technical direction. What you’ll do: - Develop strong partnerships with product teams to understand and proactively address future technology needs and current developer pain points. - Champion and drive large-scale, cross-functional initiatives that grow user visitation and engagement depth of our platform. - Act as the ultimate "advocate" for engineers on Growth including representing needs to leadership and prioritizing projects on the platform teams that ensure high quality capabilities and a world-class Pinner experience. - Scale your leadership through both direct mentorship and via best practices, processes, training and tools. - Ensure solid technical plans are in place for projects within Growth via direct review or delegation. - Be the technical point of contact for decisions that impact the whole Pinterest platform via the Growth initiatives and for cross-functional partners for an 125+ member org. Qualifications - Deep expertise building large scale ML systems at scale with modern frameworks. - Knowledge of (and a passion for) building responsible and quality first discovery surfaces to drive user visitations. - Track record of innovating and delivering large, cross-functional projects across multiple organizations. - Strong written and verbal communication skills and proven ability to collaborate cross-functionally. - Degree in Computer Science, Machine Learning, Statistics or related field. - 10+ years of professional experience as a hands-on engineer and technical leader leading multiple projects. Requirements - This role will need to be in the office for in-person collaboration 1-2 times every 6-months and therefore can be situated anywhere in the country. - This position is not eligible for relocation assistance. Benefits - Visit our PinFlex page to learn more about our working model.

• Design, build, and maintain large-scale multilingual datasets across high- and low-resource languages • Develop data pipelines for collection, cleaning, normalization, deduplication, and labeling • Implement quality filters using statistical, heuristic, and model-based methods • Work with researchers to define language coverage, benchmarks, and evaluation metrics • Analyze dataset bias, coverage gaps, and failure modes across regions and scripts • Support training, fine-tuning, and distillation workflows with high-quality multilingual data • Continuously iterate on datasets based on model performance and real-world usage