Job Description
Welcome to the future of intelligence. Aether Dynamics is pioneering the next generation of generative AI and adaptive machine learning systems. We are seeking a visionary Senior AI & Machine Learning Engineer to join our elite engineering team in San Francisco. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and neural networks, this is your opportunity to shape the landscape of 2026 and beyond.
Why Join Us?
- Impactful Work: Build algorithms that redefine human-computer interaction.
- Future-Ready: Work in a cutting-edge environment focused on the technology trends of 2026.
- Competitive Compensation: Base salary + equity package.
We are looking for a technical leader who thrives in ambiguity and enjoys solving complex, large-scale problems.
Responsibilities
- Design & Architecture: Lead the design and implementation of scalable machine learning infrastructure and deep learning models.
- Model Optimization: Fine-tune and optimize large pre-trained models for specific industry applications to ensure high accuracy and low latency.
- Research & Development: Stay at the forefront of AI research, evaluating new papers and techniques (e.g., Transformers, Diffusion Models) to integrate into our production stack.
- Collaboration: Partner with cross-functional teams of data scientists, software engineers, and product managers to deliver innovative AI solutions.
- MLOps: Establish robust CI/CD pipelines for model training, deployment, and monitoring to ensure system reliability.
Qualifications
- Education: MS or PhD in Computer Science, Statistics, or a related field.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of NLP and computer vision principles.
- Experience: 5+ years of professional experience in machine learning engineering or research.
- Tools: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to debug complex systems and improve model performance under production constraints.