Job Description
Shape the Future with Nexus 2026
We are seeking a visionary Senior Machine Learning Engineer to join our elite team in San Francisco. At Nexus 2026, we aren't just building software; we are architecting the intelligence that will define the next decade of human-machine interaction. If you possess a deep passion for algorithmic innovation and want to solve complex problems at scale, this is your opportunity to lead the charge.
In this role, you will work on proprietary large language models and predictive analytics platforms, pushing the boundaries of what is possible in artificial intelligence. You will collaborate with world-class researchers and product strategists to deliver solutions that are not only technically superior but commercially transformative.
Responsibilities
- Design, develop, and deploy scalable machine learning models and data pipelines that drive core product features.
- Conduct cutting-edge research to implement state-of-the-art algorithms, including Deep Learning and Natural Language Processing.
- Collaborate with cross-functional teams (Data Science, Product, Engineering) to translate business requirements into technical solutions.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Optimize model inference latency and accuracy to ensure seamless user experiences.
- Stay abreast of the latest industry trends in AI, ML, and cloud computing to integrate best practices.
Qualifications
- 5+ years of professional experience in Machine Learning, Deep Learning, or a related field.
- Proficiency in programming languages such as Python, PyTorch, or TensorFlow.
- Strong understanding of statistical modeling, distributed computing, and large-scale data processing.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- BS, MS, or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Demonstrated ability to ship production-grade software and manage end-to-end model lifecycles.