Job Description
We are looking for a visionary Senior AI Engineer to join our elite engineering team. At Nexus Future Tech, we are building the operating systems for tomorrow's world, leveraging cutting-edge generative AI and neural architectures. If you are passionate about pushing the boundaries of what is possible in artificial intelligence and want to leave a lasting impact on the tech landscape of 2026 and beyond, we want to meet you.
In this role, you will lead the design and implementation of scalable machine learning systems that power our core products. You will work in a fast-paced, collaborative environment with some of the brightest minds in the industry.
Why Join Us?
- Work on Next-Gen AI technologies that will define the future.
- Competitive compensation package with equity options.
- Flexible remote-first culture with premium office amenities in the heart of San Francisco.
- Unlimited PTO and continuous learning budget.
Responsibilities
- Lead Architecture: Design and architect scalable, high-performance AI/ML systems and pipelines capable of handling petabytes of data.
- Model Development: Research, develop, and deploy state-of-the-art machine learning models, with a focus on Deep Learning and Generative AI.
- Team Mentorship: Guide junior engineers and data scientists, conducting code reviews and fostering a culture of technical excellence.
- Product Integration: Collaborate closely with product managers and software engineers to integrate AI models into production applications seamlessly.
- R&D: Stay at the forefront of AI trends, exploring novel architectures like Transformers and Graph Neural Networks.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering or data science, with a strong focus on AI.
- Programming: Proficiency in Python, PyTorch, or TensorFlow.
- Algorithms: Deep understanding of statistical learning, optimization techniques, and large language models.
- Cloud: Experience deploying models on cloud platforms (AWS, GCP, or Azure).