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Junior Full Stack Machine Learning Engineer Jobs in Alabama

AV/BlueHalo is seeking an experienced Full Stack Software Engineer. The ideal candidate will work closely with the Product Owners in completing complex development taskings while aiding in the daily ...

... using full-stack software development, the Microsoft Power Platform, and/or SharePoint Online ... Mentor junior developers throughout the development process, giving valuable feedback during code ...

As a Full Stack Software Engineer your duties will include the following, but are not limited to: * Design, develop, test, and maintain full-stack software applications. * Develop web applications ...

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Junior Full Stack Machine Learning Engineer information

What is the difference between Junior Full Stack Machine Learning Engineer vs Junior Data Scientist?

AspectJunior Full Stack Machine Learning EngineerJunior Data Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops end-to-end ML applications, works on both backend and frontendAnalyzes data, builds models, and visualizes insights, mainly in data analysis tools
Employer & Industry UsageTech companies, startups, AI-focused firmsResearch institutions, tech companies, finance, healthcare

While both roles involve working with data and machine learning, the Junior Full Stack Machine Learning Engineer focuses on building complete applications with ML components, including frontend and backend development. The Junior Data Scientist primarily analyzes data, creates models, and provides insights without necessarily developing full applications.

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What cities in Alabama are hiring for Junior Full Stack Machine Learning Engineer jobs?

Cities in Alabama with the most Junior Full Stack Machine Learning Engineer job openings:

Machine Learning Engineer

Tuscaloosa, AL

Full-time

Re-posted 24 days ago


Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems