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 ...
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 ...
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 ...
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 ...
Machine Learning Engineer
Kenosha, WI · On-site
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 ...
Machine Learning Engineer
Kenosha, WI · On-site
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 ...
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 ...
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 ...
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 ...
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 ...
Machine Learning Engineer
Green Bay, WI · On-site
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 ...
Machine Learning Engineer
Green Bay, WI · On-site
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 ...
Machine Learning Engineer
Madison, WI · On-site
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 ...
Machine Learning Engineer
Madison, WI · On-site
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 ...
Machine Learning Engineer
Whitewater, WI · On-site
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 ...
Machine Learning Engineer
Whitewater, WI · On-site
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 ...
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 ...
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 ...
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 ...
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 ...
Machine Learning Engineer
Milwaukee, WI · On-site
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 ...
Machine Learning Engineer
Milwaukee, WI · On-site
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 ...
Machine Learning Engineer
Racine, WI · On-site
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 ...
Machine Learning Engineer
Racine, WI · On-site
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 ...
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 ...
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 ...
Machine Learning Engineer
Menomonie, WI · On-site
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 ...
Machine Learning Engineer
Menomonie, WI · On-site
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 ...
Knowledge of fine-tuning techniques (LoRA, PEFT, RLHF). * Experience with knowledge graphs or GraphRAG. * Familiarity with LLMOps, CI/CD pipelines, and model governance. Please send us your updated ...
Knowledge of fine-tuning techniques (LoRA, PEFT, RLHF). * Experience with knowledge graphs or GraphRAG. * Familiarity with LLMOps, CI/CD pipelines, and model governance. Please send us your updated ...
Rlhf information
What is an RLHF job?
An RLHF (Reinforcement Learning with Human Feedback) job involves training AI models using human feedback to improve their responses. Professionals in this role analyze model outputs, provide evaluations, and refine AI behavior through reinforcement learning techniques. These roles are common in AI research, content moderation, and chatbot development.
What are the key skills and qualifications needed to thrive as a Reinforcement Learning from Human Feedback (RLHF) engineer, and why are they important?
To thrive as an RLHF Engineer, you need a strong background in machine learning, reinforcement learning, and programming (often Python), typically supported by an advanced degree in computer science or a related field. Experience with ML frameworks (such as TensorFlow or PyTorch), data annotation tools, and familiarity with large language models are typically required. Strong analytical thinking, collaboration, and clear communication are essential soft skills to succeed in research-driven, interdisciplinary teams. These skills and qualities are crucial for developing safe, effective AI systems that integrate human feedback and adapt to complex real-world tasks.
What are some common challenges faced by professionals working in Reinforcement Learning from Human Feedback (RLHF) roles?
Professionals in RLHF roles often encounter challenges related to data quality and alignment between human feedback and model behavior. Collecting consistent, unbiased feedback from human annotators can be complex, and ensuring that the reinforcement learning model interprets this feedback correctly requires careful design of reward functions and training protocols. Additionally, balancing the need for rapid experimentation with maintaining rigorous evaluation standards is crucial. Collaboration with interdisciplinary teams, including data scientists, ML engineers, and domain experts, is common to address these challenges and improve model alignment.
What is the difference between Rlhf vs Rn?
| Aspect | Rlhf | Rn |
|---|---|---|
| Required Credentials | Licensed healthcare professional, often with specialized training in mental health or behavioral health | Licensed practical nurse or registered nurse, with nursing licensure and possibly additional certifications |
| Work Environment | Behavioral health facilities, clinics, hospitals, or community health settings | Hospitals, clinics, long-term care facilities, and community health settings |
| Employer & Industry Usage | Behavioral health and mental health services | General healthcare and nursing services |
| Common Search & Comparison | Rlhf vs Rn | Rlhf vs Rn |
While Rlhf (Registered Licensed Mental Health Facilitator) focuses on mental health support and behavioral health interventions, Rn (Registered Nurse) provides broader nursing care across various medical settings. Both roles require licensure, but Rlhf specializes in mental health, whereas Rn covers general patient care.
What are the most commonly searched types of Rlhf jobs in Wisconsin?
The most popular types of Rlhf jobs in Wisconsin are:
What are popular job titles related to Rlhf jobs in Wisconsin?
For Rlhf jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Rlhf jobs in Wisconsin look for?
The top searched job categories for Rlhf jobs in Wisconsin are:
Full-time
Re-posted 17 days ago
Job description
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Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch
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Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration
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Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues
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Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy
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Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams
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Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics
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Implement methods from recent ML papers quickly and turn them into production-grade systems