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Internship Research Assistant Machine Learning Jobs in Kansas

Bespoke Labs

Machine Learning Engineer

Topeka, KS

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 ...

Bespoke Labs

Machine Learning Engineer

Kansas City, KS · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Wichita, KS

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 ...

Bespoke Labs

Machine Learning Engineer

Pittsburg, KS · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Wichita, KS · On-site

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 ...

Block

This is not a traditional internship. You'll own a research problem end-to-end: framing the ... Strong foundations in modern machine learning, including deep learning, optimization ...

Block

This is not a traditional internship. You'll own a research problem end-to-end: framing the ... Strong foundations in modern machine learning, including deep learning, optimization ...

Block

This is not a traditional internship. You'll own a research problem end-to-end: framing the ... Strong foundations in modern machine learning, including deep learning, optimization ...

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Internship Research Assistant Machine Learning information

What is the difference between Internship Research Assistant Machine Learning vs Research Assistant Data Science?

AspectInternship Research Assistant Machine LearningResearch Assistant Data Science
Required CredentialsUndergraduate or graduate in CS, AI, or related fieldsUndergraduate or graduate in CS, Statistics, or related fields
Work EnvironmentAcademic labs, research institutions, tech companiesAcademic institutions, research centers, industry
Employer & Industry UsageUniversities, research firms, tech companies focusing on AI/MLUniversities, research organizations, data-driven industries
Common Search & ComparisonYesYes

The Internship Research Assistant Machine Learning and Research Assistant Data Science roles share similarities in educational background and work environments. However, the Machine Learning position emphasizes AI and ML-specific skills, while Data Science focuses more on statistical analysis and data management. Both roles are common in academic and industry settings, often compared by students and professionals exploring research opportunities in data-driven fields.

What are popular job titles related to Internship Research Assistant Machine Learning jobs in Kansas?

For Internship Research Assistant Machine Learning jobs in Kansas, the most frequently searched job titles are:

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Cities in Kansas with the most Internship Research Assistant Machine Learning job openings:

Internship Research Assistant Machine Learning jobs near you

Machine Learning Engineer

Topeka, KS

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 23 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

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