RLHF Specialist

About the role

We are looking for an RLHF Specialist to design and operate reinforcement‑learning‑from‑human‑feedback pipelines that improve the safety, factuality and alignment of large language models. The role is fully remote and collaborates closely with machine‑learning engineers and annotation teams.

Key responsibilities

• Generate high‑quality preference data by ranking model outputs on helpfulness, honesty and harmlessness.
• Design multi‑turn prompts to stress‑test model reasoning and safety.
• Write chain‑of‑thought explanations to train reward models.
• Work with ML engineers to analyse failure modes and close data gaps.
• Develop and iterate annotation strategies, ensuring consistency across a global team.
• Probe models for biases, hallucinations and vulnerabilities, documenting findings.
• Maintain a personal benchmark set and regularly evaluate new model versions.

Required profile

• At least 2 years of experience in data annotation, model evaluation, computational linguistics or AI trust and safety.
• Strong understanding of reinforcement‑learning concepts such as PPO, trust‑region methods and reward hacking.
• Hands‑on experience fine‑tuning open‑source LLMs (e.g., Llama 2/3, Mistral, Gemma) using LoRA/QLoRA.
• Familiarity with constitutional AI or self‑alignment techniques.

Required skills

• Python programming.
• Deep‑learning frameworks: PyTorch, JAX or TensorFlow.
• Annotation platforms: LabelBox, Scale AI, Snorkel.
• Cloud services: AWS SageMaker, GCP Vertex AI.