The RLHF Specialist at Odixcity Consulting plays a pivotal role in enhancing the effectiveness of AI models through the application of Reinforcement Learning from Human Feedback (RLHF) methodologies. This position is essential as it focuses on designing and optimizing feedback pipelines that improve model performance and ensure alignment with human values. In your first few months, you will engage in generating high-quality preference data and work closely with a diverse team to analyze model behaviors, contributing significantly to the development of robust AI systems. This role not only advances your technical skills but also opens doors to innovative fields of AI research and application.
Key Responsibilities
You will generate high-quality preference data by comparing multiple model responses and ranking them based on criteria such as helpfulness, honesty, and harmlessness (HHH).
Designing complex, multi-turn prompts will be part of your daily tasks, aimed at stress-testing model behavior to expose weaknesses in reasoning or safety.
Writing detailed “chain-of-thought” explanations will be crucial for training reward models, helping to articulate why certain responses are superior to others.
You will collaborate with Machine Learning Engineers to analyze model failure modes, identifying data gaps that can be filled to enhance reinforcement learning outcomes.
Your role involves developing and iterating on annotation strategies for preference scoring and reinforcement signals, ensuring consistency across a global team.
Proactively probing models to identify vulnerabilities, biases, or hallucination patterns will be essential, with documented findings aiding in model optimization.
You will analyze edge cases where the reward model behaves unexpectedly, providing detailed feedback to ML engineers and suggesting data interventions to correct model behavior.
Additionally, you will develop templated instruction sets for larger annotation teams, translating complex reinforcement learning concepts into simple, repeatable tasks for junior reviewers.
Monitoring model performance over time will be necessary, so you will maintain a personal test set of prompts to regularly re-evaluate new model versions against historical benchmarks.
Requirements & Qualifications
A minimum of 2 years of experience in fields such as Data Annotation, Model Evaluation, or Computational Linguistics is required.
You should possess strong proficiency in Python and familiarity with deep learning frameworks like PyTorch, JAX, or TensorFlow.
A deep understanding of Reinforcement Learning concepts, including techniques such as PPO and Reward Hacking, is essential for this role.
Hands-on experience fine-tuning open-source models using techniques like LoRA/QLoRA will enhance your candidacy.
Experience with annotation tools like LabelBox or Scale AI, as well as managing human-in-the-loop workflows, is expected.
Ability to diagnose RL policy failures and adjust hyperparameters or reward structures accordingly will be a key skill in your toolkit.
What You’ll Gain
You will develop advanced skills in AI model evaluation and reinforcement learning techniques, enhancing your expertise in a cutting-edge field.
This position offers the opportunity to work with leading professionals in AI, broadening your network and collaborative skills.
Contributing to significant projects will provide you with a sense of achievement as you see your work directly impact AI performance and alignment.
As you navigate complex challenges in model optimization, you will build critical problem-solving and analytical skills that are highly valued in the tech industry.
This role sets a solid foundation for career growth, with pathways leading to advanced positions in AI research, model development, or project management.
How to Apply
If you are excited about this opportunity and meet the requirements, we encourage you to apply. Please submit your application through the following link: Apply here.
