RLHF Specialist
AI summary
Odixcity Consulting is hiring an RLHF Specialist for a remote, full-time position. The role involves designing feedback pipelines for AI alignment, generating preference data, and collaborating with ML engineers. Requires 2+ years experience in data annotation or AI/ML training data, Python proficiency, and understanding of reinforcement learning.
- Remote position open to Uganda-based applicants
- Requires 2+ years experience in AI/ML data annotation or evaluation
- Proficiency in Python and deep learning frameworks (PyTorch, JAX, TensorFlow) necessary
- Experience with annotation tools and human-in-the-loop workflows
AI job guide
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AI salary guide
Not enough public dataNot enough public salary data is available for this exact role. Before applying, prepare to ask about gross pay, benefits, contract length, probation period, transport and any allowances.
Can you qualify for this role?
- Required2+ years of relevant experienceThe job post includes a minimum experience signal.
- PreferredPractical evidence in RLHF Specialist, AI Alignment Specialist, Data AnnotationThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in Not specifiedThe vacancy is associated with this location.
- UnclearComfort with the Full Time contract termsConfirm hours, duration, probation and benefits at the original source.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest RLHF Specialist evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to BrighterMonday Uganda and the role in Not specified.
- Add concrete examples related to RLHF Specialist, AI Alignment Specialist, Data Annotation, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from BrighterMonday Uganda; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Prepare a polite question about pay, benefits and contract terms for later interview stages.
Source and safety check
- BrighterMonday Uganda
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
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Interview preparation
- What experience makes you a strong fit for this RLHF Specialist role in RLHF Specialist, AI Alignment Specialist?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Not specified under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research BrighterMonday Uganda before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
O RLHF Specialist Odixcity Consulting Today Outside Uganda Full Time Recruitment Confidential Share link Share on WhatsApp Share on LinkedIn Share on Facebook Share on Twitter Share via SMS
Experience
Location: Uganda Job descriptions &
Level: Mid level
Length: 2 years Language Requirement: English Working Hours: Full Time - 8 to 5 Applicant
in Data Annotation, Model Evaluation, Computational Linguistics, or Trust and Safety, specifically working with AI/ML training data. Strong proficiency in Python and deep learning frameworks (PyTorch, JAX, or TensorFlow). Deep understanding of Reinforcement Learning concepts (PPO, Trust Regions, Reward Hacking) and how they apply to language generation. Hands-on
fine-tuning open-source models (e.g., Llama 2/3, Mistral, gemma) using techniques like LoRA/QLoRA.
working with annotation tools (LabelBox, Scale AI, Snorkel) and managing human-in-the-loop workflows. Ability to diagnose why an RL policy collapsed and adjust hyperparameters or reward structure accordingly.
with Constitutional AI or Self-Alignment techniques. Contributions to open-source alignment libraries (TRL, Transformer Reinforcement Learning, Axolotl).
with cloud Platforms (AWS SageMaker, GCP Vertex AI). < Log In and Apply Important safety tips Do not make any payment without confirming with the BrighterMonday Customer Support Team. If you think this advert is not genuine, please report it via the Report Job link below. Report Job
Requirements
Job Title: RLHF Specialist
Location: Remote (Worldwide) Job
Summary: An RLHF Specialist is responsible for improving and aligning AI models using Reinforcement Learning from Human Feedback (RLHF) methodologies. This role focuses on designing, implementing, and optimizing feedback pipelines that enhance model performance, safety, factual accuracy, and alignment with human values.
Minimum of 2 years of
Responsibilities
Generate high-quality preference data by comparing multiple model responses and ranking them based on criteria such as helpfulness, honesty, and harmlessness (HHH). Design complex, multi-turn prompts to stress-test model behavior and expose weaknesses in reasoning or safety. Write detailed “chain-of-thought” explanations and rationales to train reward models on why specific responses are superior. Collaborate with Machine Learning Engineers to analyze model failure modes and identify data gaps that, when filled, will improve reinforcement learning outcomes. Develop and iterate on annotation strategies for preference scoring and reinforcement signals, ensuring consistency across a global team. Proactively probe models to identify vulnerabilities, biases, or hallucination patterns, documenting findings for model optimization. Analyze edge cases where the reward model behaves unexpectedly (e.g., over-indexing on verbosity or style over substance). Provide detailed feedback to ML engineers on reward model failure modes and suggest specific data interventions to correct model behavior. Develop and document templated instruction sets for larger annotation teams. Translate complex reinforcement learning concepts into simple, repeatable tasks for junior reviewers, ensuring high-quality data collection at scale. Monitor model performance over time by maintaining a personal test set of prompts. Regularly re-evaluate new model versions against historical benchmarks to track improvements or regressions in reasoning and alignment.