Job Description
Are you ready to architect the future of intelligence? Nexus Dynamics is seeking a visionary Senior AI & Machine Learning Engineer to join our elite team in San Francisco. As we pioneer next-generation generative models and autonomous systems, we need a technical leader who thrives on ambiguity and innovation.
In this role, you will not just write code; you will shape the infrastructure that powers the future. You will work with cutting-edge LLMs, optimize neural networks for real-time inference, and collaborate with world-class researchers to push the boundaries of what's possible in 2026 and beyond.
Why Join Us?
- Impactful Work: Directly influence the core algorithms that drive our flagship products.
- Future-Proof Your Career: Work at the forefront of Artificial General Intelligence (AGI) research.
- Competitive Compensation: Top-of-market salary and equity package.
Responsibilities
- Design, train, and deploy scalable machine learning models using Python and modern frameworks (PyTorch, TensorFlow).
- Collaborate with cross-functional teams (Data Science, Product, Engineering) to translate business requirements into robust AI solutions.
- Optimize existing models for high-throughput, low-latency inference environments to ensure seamless user experiences.
- Conduct rigorous code reviews, architecture planning, and mentorship of junior engineers to maintain high engineering standards.
- Stay ahead of the curve by researching and implementing the latest advancements in NLP, Computer Vision, and Reinforcement Learning.
- Monitor model performance, conduct A/B testing, and iterate on data pipelines to improve accuracy and reliability.
Qualifications
- Master’s degree or PhD in Computer Science, Engineering, Mathematics, or a related field (PhD preferred for this senior role).
- 5+ years of professional experience in Machine Learning or Artificial Intelligence development.
- Proficiency in Python, with deep knowledge of PyTorch or TensorFlow.
- Strong understanding of deep learning architectures, transformers, and large language models (LLMs).
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of shipping production-grade AI applications and handling large-scale data processing.