Job Description
Are you ready to architect the future of intelligent systems? Nexus Dynamics is seeking a visionary Senior AI Engineer to lead the development of next-generation machine learning models. You will be at the forefront of applying cutting-edge AI technologies to solve complex real-world problems in a high-growth environment.
In this role, you will not only build scalable models but also mentor a team of talented engineers, define technical roadmaps, and collaborate with cross-functional stakeholders to deliver transformative products.
Why Join Us?
- Work on state-of-the-art Large Language Models (LLMs) and Generative AI.
- Competitive compensation package with equity opportunities.
- Flexible remote-first culture with headquarters in the heart of San Francisco.
- Access to top-tier computing infrastructure and research resources.
Responsibilities
- Model Development: Design, train, and deploy scalable machine learning and deep learning models using Python, TensorFlow, and PyTorch.
- System Architecture: Collaborate with MLOps engineers to build robust, automated pipelines for model training, evaluation, and production deployment.
- Research & Innovation: Conduct cutting-edge research to explore new algorithms and techniques, specifically in Natural Language Processing (NLP) and Computer Vision.
- Performance Optimization: Optimize model inference latency and scalability to ensure real-time performance in high-traffic environments.
- Mentorship: Guide and mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
Qualifications
- Education: Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field. PhD preferred.
- Experience: 5+ years of professional experience in machine learning engineering or a similar technical role.
- Technical Skills: Strong proficiency in Python, C++, and experience with deep learning frameworks (TensorFlow, PyTorch, JAX).
- Infrastructure: Proven experience working with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to solve complex problems with data-driven approaches and translate business requirements into technical solutions.