Job Description
We are building the technological infrastructure for the next decade. Nexus Future Technologies is seeking a visionary Senior AI/ML Engineer to lead our cutting-edge initiatives for the '2026' ecosystem. If you are passionate about pushing the boundaries of artificial intelligence, large language models, and generative algorithms, this is your opportunity to define the future.
In this role, you will not just write code; you will architect the cognitive architecture of our next-generation platforms. You will work alongside world-class researchers and engineers to solve complex problems in natural language processing, computer vision, and autonomous systems.
Why join us?
- Work on projects that will shape the landscape of technology by 2026 and beyond.
- Competitive compensation and equity package in a high-growth startup environment.
- Flexible remote-first culture with a state-of-the-art office in San Francisco.
- Access to the latest hardware for AI training and inference.
Responsibilities
- Design, train, and deploy scalable machine learning models and deep neural networks.
- Lead the architecture of our 2026 platform, focusing on low-latency inference and high-throughput data pipelines.
- Collaborate with cross-functional teams to integrate AI capabilities into consumer and enterprise products.
- Research and implement novel algorithms to improve model accuracy and efficiency.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Ensure ethical AI practices and compliance with data privacy regulations.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in machine learning, AI, or a similar technical domain.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Strong understanding of NLP, LLMs, or Computer Vision.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Proven track record of shipping production-grade machine learning models.