Staff AI/ML Engineer, Deep Agentic Reasoning Engineer (Lorenz Labs)

• What   You’ll   Do
• Develop and Evaluate Models: build   and evaluate deep learning models for multimodal classification and reasoning tasks.
• Full Research Stack: Engage in the full research lifecycle, including stakeholder discussions, data collection, annotation, preprocessing, model training, and rigorous evaluation.
• Prototype and Innovate: Prototype models in Python (using   PyTorch/TensorFlow) and implement core reasoning improvements, including mid-training and post-training techniques for multimodal models.
• Knowledge Distillation: Apply distillation techniques to transfer reasoning capabilities from larger models to smaller, efficient models.
• Dissemination: Document experiments and results through internal reports, blog posts, and publications in top-tier conferences and workshops.
• AI/ML Expertise: 6 years of experience releasing AI/ML products.
• Educational Background: PhD in AI-related fields such as NLP, Computer Vision, Speech Processing, or related discipline.
• Research Excellence: Strong publication record in top-tier venues (e.g.,   NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, CVPR).
• Core Technical Expertise
• Proficiency   in training, post-training, and evaluating transformer-based models with deep understanding of training dynamics, architecture modifications, and optimization techniques
• Strong   expertise   in supervised fine-tuning, RL (e.g., GRPO), and advanced post-training techniques for reasoning models, including hands-on experience with RL implementation and policy optimization
• Experience in implementing and   modifying   transformer architectures (encoder/decoder variants, attention mechanisms, positional encodings)
• Advanced knowledge of techniques including Chain-of-Thought (CoT), agentic tree search methods, continual learning, etc.
• Multi-agent orchestration, task decomposition, and experience with  both static frameworks (e.g.,   LangGraph ) and dynamic multi-agent harnesses for complex reasoning workflows
• Growth Mindset
• Appetite for continuous learning with   demonstrated   ability to quickly adapt to new research directions
• Resourcefulness in exploring and implementing emerging paradigms such as JEPA training, world models, diffusion-based generation, and agentic harnesses
• Interest in frontier applications including edge AI deployment, multi-agent orchestration, deep research automation
• EEO is the Law: Notice of Applicant Rights Under the Law .
• Job Req Type: Experienced
• Required Travel: Yes, 10% of the time
• Shift Type: 1st Shift/Days

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