Senior LLM Researcher - Alignment

For one of our clients, we are looking for a Senior LLM Researcher - Alignment to help build reliable, safe, and high-performing language models. In this role, you will design and implement alignment, sanitization, evaluation, and guardrailing pipelines that enable the delivery of trustworthy AI at scale. You will also play a key role in advancing Arabic-focused language model capabilities, including dialectal coverage and culturally relevant safety considerations. Key Responsibilities

• Own alignment strategies, including:
• Supervised fine-tuning (SFT)
• Preference optimization, such as DPO and RLHF
• Reward modeling
• Rejection sampling
• Constrained decoding
• Design and build end-to-end alignment pipelines covering data curation, training, preference optimization, and evaluation.
• Develop data pipelines for filtering, augmentation, preference-data collection, and quality control.
• Build model sanitization workflows, including:
• PII removal
• Data redaction
• Prompt and response scrubbing
• Harmful-pattern filtering
• Implement runtime guardrails using policy models, classifiers, rule engines, retrieval-based safety checks, and rate controls.
• Establish red-teaming and adversarial testing programs, including incident taxonomies and response playbooks.
• Define and maintain evaluation frameworks covering safety, robustness, task performance, drift, latency, and telemetry.
• Expand Arabic language coverage across dialects, domains, and culturally relevant safety scenarios.
• Partner with product, platform, and engineering teams to move research into production.
• Mentor engineers and researchers while developing internal documentation, playbooks, and best practices.
• Lead major AI initiatives that improve model helpfulness, harmlessness, faithfulness, reliability, and cost efficiency. Requirements
• Proven research or industry experience training and aligning large language models.
• Strong hands-on experience with SFT and preference optimization, including RLHF, DPO, or reward modeling.
• Strong machine learning theory and engineering capabilities.
• Demonstrated experience in LLM safety and security, including guardrails, classifiers, adversarial prompting, or PII handling.
• Experience designing evaluation frameworks for model quality, safety, robustness, and reliability.
• Track record of taking research from experimentation through production, supported by clear metrics and documentation.
• Strong programming skills in Python and experience with modern deep learning frameworks, particularly PyTorch.
• Ability to work cross-functionally and communicate complex research clearly.
• Experience mentoring engineers, researchers, or technical teams. Nice to Have
• Experience with red-teaming, AI policy design, or compliance in multilingual environments.
• Familiarity with Arabic NLP, including dialectal variation.
• Knowledge of retrieval-augmented generation, constrained decoding, or tool-use agents.
• Experience with inference optimization, quantization, low-latency serving, or observability.
• Familiarity with distributed training environments and large-scale model deployment.
• Experience with systems such as Hugging Face, Slurm, LlamaFactory, NeMo, RLHF/DPO frameworks, vector databases, and policy or guardrail services.
• Experience working with H200 or similar GPU infrastructure.