Principal ML with Deep Learning

We are seeking a Principal ML Systems & Alignment Engineer to lead the research, development, and deployment of advanced machine learning systems, with a focus on AI alignment, model behavior, safety, and reliability. The ideal candidate will have deep research expertise in ML/Deep Learning and hands-on experience building, evaluating, and aligning large-scale AI models., * Lead research and development of advanced ML and deep learning systems.

• Design and implement AI alignment techniques to improve model behavior, reliability, and safety.
• Work on LLM training, fine-tuning, optimization, and evaluation.
• Apply techniques such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), and preference modeling.
• Develop model evaluation frameworks, benchmarks, and safety testing methodologies.
• Investigate model robustness, hallucinations, bias, interpretability, and failure modes.
• Optimize large-scale ML training and inference systems.
• Collaborate with research scientists, ML engineers, and cross-functional teams.
• Provide technical leadership and mentor engineering and research teams.

• PhD in Deep Learning, Machine Learning, AI, or a related field - Mandatory.
• Strong research background in machine learning, deep learning, and neural networks.
• Expertise in LLMs, Transformer architectures, and generative AI.
• Experience with AI alignment, RLHF, DPO, reward modeling, or preference optimization.
• Strong programming skills in Python and frameworks such as PyTorch or TensorFlow.
• Experience with model training, fine-tuning, evaluation, and optimization.
• Knowledge of AI safety, model robustness, interpretability, and responsible AI.
• Experience with distributed training and large-scale ML systems is preferred.
• Strong research publication record in relevant AI/ML conferences or journals is a plus.

Preferred Qualifications

• Experience working on frontier AI models or advanced LLM research.
• Publications in NeurIPS, ICML, ICLR, ACL, or similar conferences.
• Experience with large-scale GPU infrastructure and distributed computing.
• Experience developing alignment and safety evaluation frameworks., A research-oriented ML/AI expert with a mandatory PhD, strong hands-on deep learning experience, and proven expertise in LLM alignment, model evaluation, and large-scale ML systems.