Staff Machine Learning Engineer

Job not on LinkedIn

🕒 March 28

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Terra

WebsiteLinkedInAll Job Openings

51 - 200 employees

🛡️ Insurance

📋 Compliance

☁️ SaaS

Insurance • Compliance • SaaS

Terra is a provider of cloud-native software solutions that simplify and enhance the management of Workers' Compensation claims and policies. Their platform streamlines operations through automated workflows, integrates with medical services, and offers robust analytics, helping users improve efficiency and ensure compliance. Designed for third-party administrators, insurers, and self-insured groups, Terra's solutions allow for effective claims handling and policy management without the burden of traditional legacy systems.

📋 Description

• Design, train, test, and iterate on diffusion models for 3D geological models • Design, train, test, and iterate on an approach to for conditioning generation on geophysical data and other observations • Inform the generation of synthetic data to improve model performance • Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

🎯 Requirements

• Extensive PyTorch Experience • Deep understanding of PyTorch, including writing custom modules, optimizing training, and debugging issues in large-scale models. • Expertise in Developing Large Deep Learning Models from Scratch • Proven ability to design, implement, and train complex deep learning architectures from the ground up. • Data Curation Skills • Hands-on experience in creating, cleaning, and maintaining high-quality datasets tailored for machine learning applications. • Strong Software Engineering and Design Experience • Proficient in software development best practices, including version control, testing, and code optimization. • Familiarity with designing scalable and maintainable systems. • Bonus points if you: • Experience with Generative Models • Familiarity with generative architectures, particularly diffusion models, and an emphasis on posterior sampling methods. • Knowledge of Transformer Architectures • Experience building and training transformers, especially in applications involving 3D data. • Scaling Models Across Large GPU Clusters • Expertise in parallelizing models across multiple GPUs and optimizing distributed training pipelines. • Cloud Infrastructure Expertise • Experience setting up, managing, and optimizing cloud environments for machine learning workloads, including provisioning resources and managing costs.

🏖️ Benefits

• Health insurance • Flexible work arrangements • Professional development opportunities