Atna.AI is a fast-growing, research-driven AI organization dedicated to pushing the boundaries of machine learning and computer vision.
We are seeking a strategic and technical Engineering Director to lead our core AI engineering team and drive our synthetic media detection initiatives. In this role, you will lead high-impact technical programs, mentor top-tier engineering talent, dissect state-of-the-art neural network architectures, and customize underlying model components to solve complex classification, segmentation, and forensic challenges. You will bridge the gap between cutting-edge theoretical research, technical leadership, and scalable, real-world deployment.
As the AI Engineering Director, you will provide strategic vision, technical leadership, and direct team management for an elite group of AI engineers. You will oversee the complete lifecycle of our synthetic media detection initiatives, guide architectural decisions, optimize delivery pipelines, and ensure your team translates complex research into performant, production-ready systems.
Lead, mentor, and grow the engineering team, providing ongoing technical direction, career development, and performance guidance for direct reports
Direct sprint planning, technical roadmaps, and resource allocation to effectively balance long-term foundational R&D with near-term delivery goals
Partner with executive leadership, product teams, and infrastructure engineers to align AI strategy with broader business objectives
Architecture & Fine-Tuning: Oversee and architect foundation models for deepfake image detection, image-to-image edit segmentation, and video/audio manipulation analysis
Core Model Engineering: Guide the deconstruction and customization of core architectural components — specifically Transformer encoders/decoders, multi-head attention blocks, and latent space embeddings
Advanced Neural Networks: Drive the implementation and iteration of CLIP-based multimodal models, Vision Transformers (ViT), and advanced U-Net architectures for cross-modal forensic analysis and spatial segmentation
Loss Function & Optimization: Oversee the development of specialized loss functions (e.g., combined BCE + Dice, contrastive loss) and optimize gradient flows for subpixel boundary accuracy
Production Refactoring & MLOps: Ensure experimental PyTorch research code is refactored into highly optimized, production-ready Python pipelines with CPU/GPU inference optimization
Deployment & Infrastructure: Direct the deployment strategy for containerized AI microservices using Docker across multi-node Swarm clusters with GitHub Actions CI/CD pipelines
Engineering Management: Proven leadership in managing technical teams, setting engineering standards, and driving execution
Deep Learning Stack: Deep proficiency in Python, PyTorch, NumPy, and CUDA tensor optimization
Model Architectures: Extensive expertise in Vision Transformers (ViT), CLIP embeddings, Auto-Encoders, and U-Net
DevOps & Infrastructure: Command of Docker, Docker Swarm, GitHub Actions CI/CD, Poetry, and FastAPI/Flask APIs
Advanced expertise in latent space manipulation, transfer learning, and PCA
Experience combining graph algorithms with neural network probability maps
Demonstrated leadership in structuring multi-tiered engineering teams