Artificial Intelligence Engineer

About the CompanyAtna.ai is seeking a self-directed R&D AI Engineer with 2-3 years of dedicated, hands-on experience manipulating foundation models. In this role, you will take ownership of our synthetic media detection initiatives, dissecting state-of-the-art neural network architectures and customizing their underlying components to solve complex classification and segmentation challenges. As an independent researcher and solo contributor, you will bridge the gap between cutting-edge theoretical research and scalable, real-world deployment.About the RoleIn this role, you will take ownership of our synthetic media detection initiatives, dissecting state-of-the-art neural network architectures and customizing their underlying components to solve complex classification and segmentation challenges.ResponsibilitiesArchitect, modify, and fine-tune foundation models for deepfake image detection, image-to-image edit segmentation, and complex video/audio manipulation analysis.Deconstruct and rebuild core architectural components—specifically Transformer encoders/decoders, multi-head attention blocks, and latent space embeddings—rather than relying on out-of-the-box API implementations.Implement and iterate upon CLIP-based multimodal models, Vision Transformers (ViT), and advanced U-Net architectures for cross-modal forensic analysis and high-precision spatial segmentation.Engineer specialized loss functions (e.g., combined BCE + Dice, contrastive loss) and optimize gradient flows to achieve subpixel boundary accuracy in synthetic media detection.Refactor experimental PyTorch research code into highly optimized, production-ready Python pipelines, ensuring strict dependency management and CPU/GPU inference optimization.Containerize AI microservices using Docker and manage seamless deployments across multi-node Swarm clusters utilizing GitHub Actions CI/CD pipelines.QualificationsExperience: 3-5 years of applied experience specifically training, fine-tuning, and modifying foundation models.Core ML/DL Stack: Deep proficiency in Python, PyTorch, NumPy, and CUDA tensor optimization.Architectural Mastery: Intimate knowledge of Vision Transformers (ViT), CLIP embeddings, Auto-Encoders, U-Net, and self-supervised learning (SSL) paradigms.Deployment & Infrastructure: Strong capabilities in Python environment management (e.g., Poetry), Docker, Docker Swarm, GitHub Actions, and RESTful API development (Flask/FastAPI) for high-throughput, cluster-based environments.Advanced Techniques: Expertise in latent space manipulation, transfer learning, principal component analysis (PCA), and integrating advanced algorithmic logic (e.g., graph-based shortest-path algorithms) with neural network probability maps.Research Acumen: Demonstrated ability to autonomously read, comprehend, and translate complex AI research papers into performant, scalable code without external guidance.Required SkillsDeep proficiency in Python, PyTorch, NumPy, and CUDA tensor optimization.Intimate knowledge of Vision Transformers (ViT), CLIP embeddings, Auto-Encoders, U-Net, and self-supervised learning (SSL) paradigms.Strong capabilities in Python environment management (e.g., Poetry), Docker, Docker Swarm, GitHub Actions, and RESTful API development (Flask/FastAPI).Preferred SkillsExpertise in latent space manipulation, transfer learning, principal component analysis (PCA).Integrating advanced algorithmic logic (e.g., graph-based shortest-path algorithms) with neural network probability maps.