Job Description
We are Aethelgard Dynamics, a pioneering technology firm dedicated to defining the landscape of Artificial Intelligence for the next decade. As we accelerate our roadmap toward the revolutionary year of 2026, we are seeking a visionary Lead AI Architect to spearhead our core research and infrastructure projects.
In this role, you will not just implement existing models; you will architect the neural frameworks that will power autonomous systems, predictive analytics, and generative AI solutions. We offer a competitive salary, equity packages, and the chance to work on cutting-edge technology that will shape the future of human-machine interaction.
Why Join Us?
- Work on mission-critical projects for the 2026 technology horizon.
- Flexible remote-first policy with quarterly team offsites in San Francisco.
- Top-tier health benefits and continuous learning stipends.
- Collaborative environment with industry leaders in deep learning.
Responsibilities
- Design and deploy scalable, fault-tolerant machine learning infrastructure capable of handling petabyte-scale data.
- Lead the research and development of novel deep learning architectures and reinforcement learning algorithms.
- Optimize model inference latency and throughput for real-time applications.
- Establish best practices for MLOps, including model versioning, monitoring, and automated retraining pipelines.
- Collaborate with product managers and engineering teams to translate business requirements into technical AI solutions.
- Provide technical mentorship and guidance to a team of junior data scientists and ML engineers.
- Stay ahead of the curve by publishing research and attending leading AI conferences.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Minimum of 5 years of professional experience in Machine Learning Engineering or Applied Research.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of deep learning architectures (Transformers, GANs, RNNs) and large language models.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Proven track record of deploying production-grade AI models with high accuracy and efficiency.
- Excellent communication skills and the ability to articulate complex technical concepts to non-technical stakeholders.