RLHF Specialist

About the role

The RLHF Specialist will design and operate feedback pipelines that improve large language models through Reinforcement Learning from Human Feedback. Working remotely, you will collaborate with ML engineers to enhance model safety, factual accuracy, and alignment with human values.

Key responsibilities

• Generate high‑quality preference data by ranking model responses on helpfulness, honesty, and harmlessness.
• Design multi‑turn prompts to stress‑test reasoning and safety, and write chain‑of‑thought explanations for reward‑model training.
• Partner with engineers to analyse failure modes, identify data gaps, and propose data‑driven interventions.
• Develop and iterate annotation strategies, ensuring consistency across a global team.
• Probe models for biases, hallucinations, or vulnerabilities and document findings.
• Maintain a personal benchmark set, regularly re‑evaluating new model versions.

Required profile

• At least 2 years of experience in data annotation, model evaluation, computational linguistics, or AI trust & safety.
• Strong background in reinforcement‑learning concepts such as PPO, trust‑region methods, and reward hacking.
• Hands‑on experience fine‑tuning open‑source models (e.g., Llama 2/3, Mistral, Gemma) using LoRA/QLoRA.
• Familiarity with cloud platforms like AWS SageMaker or GCP Vertex AI.

Required skills

• Python programming
• Deep‑learning frameworks: PyTorch, JAX, TensorFlow
• LoRA / QLoRA fine‑tuning techniques
• Annotation tools: LabelBox, Scale AI, Snorkel
• Cloud services: AWS SageMaker, GCP Vertex AI