Job Description
Who We Are
Aether Dynamics is pioneering the next generation of artificial intelligence, focusing on scalable generative models that redefine human-computer interaction. We are a remote-first team of visionaries, engineers, and researchers dedicated to building the neural architecture for the future. If you are passionate about Large Language Models (LLMs), diffusion models, and the ethical implications of AI, we want to meet you.
The Role
We are seeking a highly skilled Senior Generative AI Engineer to lead our research and engineering efforts. You will be responsible for designing, training, and deploying state-of-the-art AI models that power our flagship products. You will work closely with our product and data science teams to translate theoretical research into practical, high-performance applications.
Why Join Us?
- Impactful Work: Build AI systems that directly influence millions of users worldwide.
- Top-Tier Compensation: Competitive salary plus equity package.
- Flexible Work: Fully remote culture with flexible hours.
- Continuous Learning: Access to the latest GPUs, cloud credits, and conferences.
Responsibilities
- Design and implement novel architectures for Large Language Models (LLMs) and multimodal generative systems.
- Optimize model inference pipelines to reduce latency and operational costs while maximizing output quality.
- Perform rigorous experimentation and fine-tuning of pre-trained models (e.g., GPT-4, Llama 3) on domain-specific datasets.
- Collaborate with the data engineering team to ensure high-quality, diverse training data and robust data pipelines.
- Conduct research into emerging AI trends, including Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI.
- Ensure all deployed models adhere to safety guidelines, bias mitigation standards, and privacy regulations.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field; PhD preferred.
- 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of transformer architectures, attention mechanisms, and vector databases (e.g., Pinecone, Milvus).
- Experience with model deployment frameworks such as FastAPI, Docker, and Kubernetes.
- Proven track record of publishing in top-tier AI conferences (NeurIPS, ICML, ACL) or open-sourcing high-quality models.