Machine Learning Engineer
Pomona, CA · On-site
... RLHF, PPO, DPO, or reward model training -- and understanding of how training data quality affects model behavior Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or ...
Machine Learning Engineer
Pomona, CA · On-site
... RLHF, PPO, DPO, or reward model training -- and understanding of how training data quality affects model behavior Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or ...
Experience with multiagent coordination, RLHF/RLAIF, or feedback loops preferred. Knowledge of safety filters, jailbreak resistance, and redteam tactics preferred. Automotive or financial services ...
Experience with multiagent coordination, RLHF/RLAIF, or feedback loops preferred. Knowledge of safety filters, jailbreak resistance, and redteam tactics preferred. Automotive or financial services ...
Principal Solutions Architect
Irvine, CA · On-site
Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF). * Size and configure DGX SuperPOD, HGX H/B ...
Principal Solutions Architect
Irvine, CA · On-site
Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF). * Size and configure DGX SuperPOD, HGX H/B ...
Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF). * Size and configure DGX SuperPOD, HGX H/B ...
Architect multi-node GPU clusters optimized for large language model (LLM) pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF). * Size and configure DGX SuperPOD, HGX H/B ...
Sr. AI Agent / Prompt Engineer
Irvine, CA · On-site
$132K - $204K/yr
... RLHF/RLAIF, or feedback loops preferred. • Knowledge of safety filters, jailbreak resistance, and red - team tactics preferred. • Automotive or financial services industries preferred. • ...
Sr. AI Agent / Prompt Engineer
Irvine, CA · On-site
$132K - $204K/yr
... RLHF/RLAIF, or feedback loops preferred. • Knowledge of safety filters, jailbreak resistance, and red - team tactics preferred. • Automotive or financial services industries preferred. • ...
Sr. AI Agent / Prompt Engineer
Irvine, CA · On-site
... RLHF/RLAIF, or feedback loops preferred. • Knowledge of safety filters, jailbreak resistance, and red - team tactics preferred. • Automotive or financial services industries preferred. • ...
Sr. AI Agent / Prompt Engineer
Irvine, CA · On-site
... RLHF/RLAIF, or feedback loops preferred. • Knowledge of safety filters, jailbreak resistance, and red - team tactics preferred. • Automotive or financial services industries preferred. • ...
Rlhf information
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 are RLHF jobs?
RLHF stands for Reinforcement Learning from Human Feedback. RLHF jobs typically involve roles where professionals help train artificial intelligence (AI) systems, especially large language models, by providing feedback, curating datasets, designing reward models, or developing algorithms that enable AI to learn effectively from human input. These jobs may include positions such as machine learning engineers, data annotators, AI trainers, and research scientists. The goal of RLHF work is to improve the alignment of AI behavior with human values and expectations by incorporating direct human feedback into the training process.
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 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 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 job categories do people searching Rlhf jobs in Riverside, CA look for? The top searched job categories for Rlhf jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Rlhf jobs? Cities near Riverside, CA with the most Rlhf job openings:
Full-time
Posted 8 days ago
Job description
About Us
We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.
We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.
What You'll Be Working On
You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.
Must-Have Skills
3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production
Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)
Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior
Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives
Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)
Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination