Job Description
Are you ready to build the future of intelligent systems? Nexus Innovations is seeking a visionary Senior AI/ML Engineer to join our elite team in San Francisco. We are not just building software; we are architecting the core logic for the next generation of autonomous solutions, aligning with our aggressive roadmap for 2026.
In this pivotal role, you will bridge the gap between theoretical machine learning research and scalable, production-grade engineering. You will have the autonomy to design algorithms that drive decision-making across our global platforms. If you are passionate about Deep Learning, Large Language Models (LLMs), and ethical AI, we want to hear from you.
Why Join Nexus?
- Work on cutting-edge AI infrastructure.
- Competitive compensation package with equity.
- Flexible work environment in the heart of San Francisco.
Apply today to shape the trajectory of AI technology.
Responsibilities
- Design & Development: Architect and deploy scalable machine learning models and deep learning pipelines using Python, TensorFlow, and PyTorch.
- Model Optimization: Fine-tune large language models and optimize inference latency to ensure high performance in real-time environments.
- MLOps Implementation: Build and maintain CI/CD pipelines for ML, integrating automated testing and monitoring for model drift.
- Research & Innovation: Stay at the forefront of AI trends, researching novel architectures and implementing them into our core product stack.
- Collaboration: Partner with data scientists and software engineers to translate complex business problems into mathematical solutions.
- Code Review: Maintain high standards of code quality and contribute to the technical roadmap for 2026 and beyond.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in building and deploying production machine learning systems.
- Programming: Proficiency in Python, SQL, and experience with distributed computing frameworks (Spark, Kubernetes).
- Libraries: Deep expertise in PyTorch, TensorFlow, or JAX.
- Problem Solving: Strong ability to debug complex algorithms and optimize system architecture.
- Communication: Excellent verbal and written communication skills for technical presentations.