DESCRIPTION

BASIC QUALIFICATIONS

• PhD, or a Master's degree and experience in solving business problems through machine learning, data mining and statistical algorithms
• Experience in building models for business application
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Strong programming proficiency in Python with production-quality code standards; deep technical expertise with PyTorch and proficiency with the modern ML stack (Pandas, NumPy, scikit-learn, Hugging Face Transformers)
• Demonstrated ability to design and execute end-to-end ML projects from research through production deployment, with experience in model monitoring and iterative improvement
• Strong expertise in modern deep learning architectures including transformers and diffusion models, with hands-on experience in training optimization techniques (distributed training, mixed precision, gradient accumulation) and model compression methods (quantization, pruning, distillation)
• Experience fine-tuning large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen)
• Proven experience developing agentic AI systems using state-of-the-art frameworks (LangChain, Strands, etc.) with ability to design tool-augmented reasoning systems, RAG systems, and advanced prompt engineering techniques (chain-of-thought, few-shot)
• Strong knowledge and hands-on experience across multiple ML domains including computer vision (object detection, segmentation, classification), natural language processing (text generation, information extraction), and multimodal learning
• Understanding of ML systems design including model serving infrastructure, A/B testing frameworks, and MLOps best practices

PREFERRED QUALIFICATIONS

• Experience in professional software development
• Experience with explainable machine learning and artificial intelligence methodologies and tools
• Experience working with large language models (GPT, LLaMA, Claude) and vision-language models (CLIP, LLaVA, Qwen) in production settings
• Experience collaborating on cross-functional ML initiatives with demonstrated impact on product metrics
• Multiple publications in top-tier venues, including co-authored papers or contributions to ML research communities
• Experience with generative AI techniques including diffusion models for image/video synthesis, autoregressive models for multimodal generation, and controllable generation systems
• Experience with specialized ML domains such as few-shot learning, meta-learning, or domain adaptation; ability to build models that handle distribution shifts or long-tail scenarios