RLHF Specialist – Remote

About the role

We are looking for an RLHF Specialist to design, implement, and optimise reinforcement‑learning‑from‑human‑feedback pipelines that improve the safety, factual accuracy, and alignment of large language models. This fully remote position works closely with machine‑learning engineers to turn human preferences into high‑quality training signals.

Key responsibilities

• Generate high‑quality preference data by ranking model responses on helpfulness, honesty, and harmlessness.
• Design multi‑turn prompts that stress‑test model reasoning and safety.
• Write chain‑of‑thought explanations to train reward models.
• Collaborate with ML engineers to analyse failure modes and identify data gaps.
• Develop and iterate annotation strategies, ensuring consistency across a global team.
• Probe models for biases, hallucinations, and vulnerabilities, documenting findings.
• Analyse edge cases where reward models behave unexpectedly and suggest data interventions.
• Translate complex RL concepts into repeatable tasks for junior reviewers.
• Maintain a personal test set of prompts to monitor model performance over time.

Required profile

• Minimum 2 years of experience in data annotation, model evaluation, computational linguistics, or AI trust & safety.
• Strong proficiency in Python and deep‑learning frameworks (PyTorch, JAX, or TensorFlow).
• Deep understanding of reinforcement‑learning algorithms (PPO, trust‑region methods, reward hacking).
• Hands‑on experience fine‑tuning open‑source models (Llama 2/3, Mistral, Gemma) with LoRA/QLoRA.
• Experience with annotation platforms such as LabelBox, Scale AI, or Snorkel.
• Familiarity with cloud ML services (AWS SageMaker, GCP Vertex AI).

Required skills

• Python
• PyTorch
• JAX
• TensorFlow
• Reinforcement Learning (PPO, Trust Regions, Reward Hacking)
• LoRA / QLoRA fine‑tuning
• Open‑source LLMs (Llama 2/3, Mistral, Gemma)
• Annotation tools (LabelBox, Scale AI, Snorkel)
• AWS SageMaker
• GCP Vertex AI