Job Description
We are seeking a visionary Senior AI Research Engineer to spearhead the 2026 Initiative, our ambitious roadmap to pioneer the next generation of autonomous intelligence systems. At Nexus Dynamics, we are building the technological infrastructure for tomorrow, and you will be the architect of the models that define our future.
In this role, you will bridge the gap between theoretical research and production deployment, working on state-of-the-art generative models and advanced neural architectures. If you are passionate about pushing the boundaries of what's possible in Artificial General Intelligence (AGI) and want to shape the trajectory of the industry, this is your opportunity.
What You Will Do:
- Drive the research and development of proprietary machine learning algorithms.
- Architect scalable, high-performance inference pipelines for complex AI models.
- Collaborate closely with product and engineering teams to translate research into real-world applications.
- Optimize model latency, accuracy, and resource efficiency in production environments.
- Mentor a growing team of research engineers and data scientists.
Responsibilities
- Lead end-to-end research projects focused on Large Language Models (LLMs) and multimodal AI.
- Implement and maintain robust CI/CD pipelines for model training and deployment.
- Analyze complex datasets to derive actionable insights and improve model performance.
- Stay at the forefront of AI innovation, integrating new techniques (e.g., reinforcement learning, federated learning) into the 2026 stack.
- Conduct rigorous code reviews and technical design reviews.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- Minimum of 5 years of experience in applied machine learning or research engineering.
- Extensive experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Deep understanding of transformer architectures, attention mechanisms, and NLP.
- Strong proficiency in Python and C++.
- Experience deploying ML models on cloud infrastructure (AWS, GCP, or Azure).