> Markdown version of [/jobs/ext/1894117-data-scientist-ii-ml-infrastructure](https://www.wearedevelopers.com/jobs/ext/1894117-data-scientist-ii-ml-infrastructure). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist II, ML Infrastructure - **Company:** The Ladders - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $114,297.0 - $235,319.0 - **Contract:** Permanent contract - **Skills:** Airflow, Python (Programming Language), Azure Machine Learning, Workflow Management Systems, Software Organization, Pytorch, Deep Learning, Information Technology, Machine Learning Operations, Software Version Control, Jenkins - **Published:** August 1, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p8u0gu00iy ## About the Role * 2+ years of applied experience in ML production as a scientist or engineer * Proficient in Python, with experience in PyTorch or similar deep learning frameworks * Passionate about building scalable tools that enhance ML organization impact * Strong knowledge of ML theory and fundamentals * Familiar with software development best practices including version control * Experience with workflow management tools like Airflow or Jenkins * Bachelor's or Master's degree in Computer Science or related field ## Description * Translate research-grade data science workflows into production ML pipelines * Apply causal inference methods for high-stakes measurement questions * Partner with ML engineers to improve tooling, metrics, and measurement methods * Leverage metadata to build frameworks that enhance platform efficiency * Design centralized ML platform tooling for model creation and trust ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [The Road to MLOps: How Verivox Transitioned to AWS](https://www.wearedevelopers.com/videos/1050-the-road-to-mlops-how-verivox-transitioned-to-aws) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company](https://www.wearedevelopers.com/videos/776-our-gitops-approach-for-deploying-an-identity-provider-and-an-api-gateway-in-a-saas-company) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)