Job Description
Shape the Future of Intelligence
At Nexus Future Systems, we are pioneering the technological landscape for 2026 and beyond. We are looking for a visionary AI/ML Engineer to join our elite research division and lead the development of next-generation Generative AI and Autonomous Systems. If you are passionate about pushing the boundaries of Large Language Models (LLMs) and building scalable, ethical AI solutions, we want to hear from you.
As part of our core team, you will work directly with industry leaders to architect models that redefine human-computer interaction. This is not just a job; it is a mission to define the future of technology.
Why Join Us?
- State-of-the-Art Tech Stack: Work with the latest in PyTorch, TensorFlow, and custom quantum-inspired algorithms.
- Equity & Impact: Competitive salary plus significant equity package.
- Flexible Environment: Hybrid work model based in the heart of San Francisco.
Responsibilities
- Design, train, and deploy cutting-edge machine learning models with a focus on Generative AI and Natural Language Processing.
- Optimize deep learning inference pipelines to reduce latency and improve cost-efficiency for high-volume applications.
- Collaborate with data scientists and software engineers to integrate AI models into scalable production environments.
- Conduct research on novel architectures and implement innovative solutions to complex technical challenges.
- Ensure model fairness, transparency, and ethical standards across all deployed systems.
- Mentor junior engineers and conduct code reviews to maintain high technical standards within the team.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in building and deploying ML models in production.
- Expert proficiency in Python, PyTorch, and TensorFlow.
- Strong understanding of MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Experience with LLMs, Transformers, or Reinforcement Learning is highly preferred.
- Demonstrated ability to translate complex research papers into practical, production-ready code.