Job Description
We are building the infrastructure for the future of intelligence. At Aether Dynamics, we are looking for a 2026 Visionary AI Engineer to spearhead the next generation of generative systems. This is not just a job; it is an opportunity to shape the trajectory of human-machine interaction in the coming decade.
In this pivotal role, you will be responsible for architecting Large Language Models (LLMs) and multi-modal AI systems that are scalable, efficient, and ethically sound. You will work in a collaborative, high-performance environment with the freedom to experiment with cutting-edge research.
What you'll do:
• Architect and fine-tune state-of-the-art generative models for enterprise solutions.
• Drive the technical roadmap for AI infrastructure, ensuring 99.99% uptime and low latency.
• Implement advanced data pipelines and reinforcement learning from human feedback (RLHF) loops.
Why join Aether Dynamics?
• Competitive equity and salary package.
• Fully remote-first culture with flexible working hours.
• Access to the latest hardware (NVIDIA H100 clusters) for rapid prototyping.
Responsibilities
- Design and implement scalable deep learning architectures for generative AI applications.
- Optimize model inference speeds for edge devices and cloud deployments.
- Lead the research and development of novel techniques in Natural Language Processing (NLP) and Computer Vision.
- Mentor a team of ML engineers and data scientists to foster a culture of innovation.
- Ensure compliance with data privacy regulations and AI ethical guidelines.
- Collaborate closely with product managers to translate technical capabilities into market-leading products.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in Machine Learning Engineering or Applied AI.
- Expert proficiency in Python, PyTorch, and TensorFlow.
- Deep experience with Large Language Models (LLMs) such as GPT, Llama, or similar architectures.
- Strong understanding of distributed systems, cloud computing (AWS/GCP), and containerization (Docker/Kubernetes).
- Demonstrated ability to publish research or contribute to open-source ML communities.