Job Description
About Aether Dynamics
We are pioneering the next generation of autonomous intelligence systems. As we accelerate towards our 2026 strategic goals, we are seeking a visionary Senior AI Research Engineer to lead the development of advanced neural architectures.
Role Overview
In this role, you will bridge the gap between theoretical AI research and scalable production systems. You will be instrumental in defining the technical roadmap for our flagship product line, ensuring our AI solutions are robust, ethical, and future-proof.
What You'll Do
- Architect and train state-of-the-art Large Language Models (LLMs) and multimodal systems.
- Optimize inference speed and memory efficiency for edge deployment in 2026 environments.
- Lead a team of junior researchers in experimental design and data analysis.
- Collaborate with product managers to define technical requirements for upcoming AI releases.
- Contribute to open-source initiatives and publish research findings in top-tier conferences.
Qualifications
- PhD or Master’s degree in Computer Science, Machine Learning, or a related field.
- Minimum of 5 years of professional experience in deep learning and NLP.
- Expert-level proficiency in Python, PyTorch, and CUDA.
- Deep understanding of transformer architectures, attention mechanisms, and RLHF.
- Strong track record of deploying ML models to production at scale.
Responsibilities
- Lead the design and implementation of next-generation artificial intelligence models targeting deployment in 2026.
- Conduct cutting-edge research in Large Language Models (LLMs) and Generative AI architectures.
- Optimize model performance, scalability, and energy efficiency for edge devices.
- Collaborate with product teams to translate futuristic concepts into actionable technical roadmaps.
- Establish best practices for AI ethics, safety, and bias mitigation in automated systems.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field.
- Proven track record of 5+ years in machine learning engineering and deep learning.
- Expert proficiency in Python, PyTorch, and TensorFlow.
- Strong understanding of neural networks, NLP, and computer vision.
- Demonstrated experience deploying ML models to production environments at scale.