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Loop Jobs in Indiana (NOW HIRING)

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 ...

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 ...

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 ...

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 ...

By owningPost-Day1 execution and creating a continuous feedback loop to improve due diligence inputs, you will ensure we deliver deal synergies rapidly while protecting the stability of our global IT ...

Leading and conducting various kinds of testing including hardware-in-the-loop (HWIL), Software-in-the-Loop (SWIL), anechoic chamber testing, and field-level radar testing * Leading root cause ...

Showing results 41-60

Loop information

What cities in Indiana are hiring for Loop jobs?

Cities in Indiana with the most Loop job openings:

Infographic showing various Loop job openings in Indiana as of August 2026, with employment types broken down into 87% Full Time, 11% Part Time, and 2% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Machine Learning Engineer

Evansville, IN

Full-time

Re-posted 28 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