Job Description
Are you ready to shape the technological landscape of 2026 and beyond? Nexus Future Systems is seeking a visionary Senior AI & Machine Learning Engineer to join our elite San Francisco R&D division. We are building the infrastructure for the next generation of intelligent applications, and we need a leader who can turn complex data into actionable breakthroughs.
In this role, you won't just maintain existing systems; you will architect the future. You will work at the intersection of deep learning, generative AI, and scalable cloud infrastructure, driving innovation that impacts millions of users globally.
Why Nexus Future Systems?
- Impactful Work: Solve real-world problems with cutting-edge AI solutions.
- Future-Ready Culture: A forward-thinking environment focused on 2026 and beyond.
- Competitive Compensation: Top-tier salary and equity packages.
Join us and be at the forefront of the AI revolution.
Responsibilities
- Architect & Deploy: Design, train, and deploy state-of-the-art machine learning models and deep neural networks using Python and PyTorch/TensorFlow.
- Model Optimization: Continuously optimize model performance, latency, and accuracy for production environments using techniques like quantization and pruning.
- Data Strategy: Lead the end-to-end data lifecycle, including data ingestion, cleaning, feature engineering, and pipeline automation.
- Collaboration: Partner with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Research: Stay ahead of the curve by researching the latest advancements in NLP, Computer Vision, or Reinforcement Learning.
- Mentorship: Mentor junior engineers and contribute to a culture of technical excellence and continuous learning.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in machine learning engineering or data science.
- Programming: Expert-level proficiency in Python (pandas, NumPy, Scikit-learn) and experience with at least one major deep learning framework.
- Cloud Expertise: Strong experience deploying models on cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure (Kubernetes, Docker, Terraform).
- Problem Solving: Proven track record of solving complex, unstructured problems with data-driven solutions.
- Communication: Excellent verbal and written communication skills for technical and non-technical audiences.