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Internship Aws Machine Learning Jobs in Hawaii (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Honolulu, HI · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Waipahu, HI · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Mililani, HI · On-site

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 ...

MANTECH

Data Scientist with Security Clearance

Aiea, HI · On-site

Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn * Skilled in data querying using SQL/NoSQL and working within cloud environments (AWS, Azure, or GCP) * High ...

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Internship Aws Machine Learning jobs near you
Infographic showing various Internship Aws Machine Learning job openings in Hawaii as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Bespoke Labs

Honolulu, HI • On-site

Full-time

Re-posted 22 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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