Job Description
We are Quantum Dynamics Inc., a pioneering technology firm at the forefront of artificial intelligence and machine learning innovation. We are seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. In this role, you will be responsible for designing and deploying state-of-the-art machine learning models that drive our next-generation products.
If you are passionate about solving complex problems, optimizing large-scale neural networks, and shaping the future of AI, we want to hear from you.
Why Join Us?
• Competitive salary and equity package.
• Work with cutting-edge technologies and industry leaders.
• Flexible remote and hybrid work options.
• Comprehensive health benefits and professional development.
Responsibilities
- Design, develop, and deploy scalable machine learning models and algorithms to solve complex business problems.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to define AI product requirements.
- Optimize and fine-tune pre-trained models (e.g., LLMs, Transformers) for specific use cases to improve accuracy and performance.
- Conduct rigorous code reviews, implement best practices, and ensure the scalability and reliability of our AI infrastructure.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Stay updated with the latest advancements in AI research and integrate relevant innovations into our development lifecycle.
Qualifications
- Master’s degree in Computer Science, Mathematics, Statistics, or a related field; PhD preferred.
- Minimum of 5+ years of professional experience in AI/ML engineering, with a strong focus on NLP or Computer Vision.
- Proficiency in programming languages such as Python, C++, or Java.
- Extensive experience with deep learning frameworks (TensorFlow, PyTorch, JAX).
- Strong understanding of MLOps, cloud platforms (AWS, GCP, or Azure), and containerization (Docker, Kubernetes).
- Demonstrated ability to deploy models to production environments and monitor their performance in real-time.