Job Description
Are you ready to architect the next generation of intelligent systems? Nexus AI Solutions is at the forefront of the AI revolution, building scalable, high-performance machine learning solutions that redefine industry standards. We are seeking a visionary Senior AI Engineer to join our elite technical team in San Francisco.
In this pivotal role, you will bridge the gap between cutting-edge research and production-grade deployment. You will have the autonomy to design architectures that solve complex problems, mentor junior engineers, and drive the technical vision of our flagship products.
Why Join Us?
- Work with state-of-the-art hardware and frameworks.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a vibrant SF office.
Responsibilities
- Model Development: Design, train, and deploy advanced machine learning models (NLP, Computer Vision, or RL) using Python, PyTorch, and TensorFlow.
- Infrastructure Optimization: Scale model inference pipelines to handle high-traffic environments, ensuring low latency and high availability.
- Research & Innovation: Stay abreast of the latest academic papers and industry trends, implementing novel algorithms to improve model accuracy.
- Collaboration: Partner with cross-functional teams (Data Science, Product, DevOps) to integrate AI capabilities into user-facing products seamlessly.
- Code Quality: Write clean, maintainable, and well-documented code, conducting rigorous code reviews to uphold engineering standards.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field with a focus on Artificial Intelligence.
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years in a senior or lead role.
- Programming: Deep expertise in Python and C++.
- Frameworks: Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Cloud: Experience deploying models on AWS, GCP, or Azure using containerization tools like Docker and Kubernetes.
- Problem Solving: Strong ability to debug complex issues and optimize computational resources for efficiency.