Job Description
Are you ready to architect the future of intelligence? Zenith Innovations is seeking a visionary Senior AI Engineer to join our elite Research & Development division in San Francisco. We are building the next generation of generative AI and autonomous systems that will redefine human-machine interaction.
In this pivotal role, you will not just implement existing models; you will design novel algorithms and push the boundaries of what is possible with Large Language Models (LLMs) and Computer Vision. You will work in a high-performance environment where your code will directly impact millions of users and drive the industry forward.
Why You Belong Here:
- Impactful Work: Deploy AI solutions that solve real-world problems at scale.
- Competitive Compensation: Base salary ranging from $180k to $250k plus equity.
- Top-Tier Benefits: Comprehensive health, dental, and vision coverage.
- Flexible Culture: Embrace a hybrid work model designed for peak productivity.
Responsibilities
- Design, train, and deploy scalable machine learning models from scratch using Python and deep learning frameworks.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to translate business requirements into technical AI solutions.
- Optimize existing models for inference speed, latency, and resource efficiency, implementing robust MLOps pipelines.
- Conduct cutting-edge research into state-of-the-art architectures to improve model accuracy, robustness, and generalization.
- Establish and enforce best practices for code quality, testing, documentation, and CI/CD workflows.
- Lead technical code reviews and mentor junior engineers to foster a culture of technical excellence and continuous learning.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field with a strong focus on Machine Learning and Artificial Intelligence.
- 5+ years of professional experience in software engineering with a strong emphasis on AI/ML model development.
- Proficiency in Python and deep familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong understanding of MLOps practices, model versioning (MLflow), and CI/CD automation.
- Excellent problem-solving skills, attention to detail, and the ability to work independently in a dynamic startup environment.