Job Description
We are on a mission to engineer the artificial intelligence systems that will define the era of 2026 and beyond. Nexus Future Systems is seeking a visionary AI Infrastructure Engineer to design scalable, resilient, and future-proof machine learning pipelines. You will work at the intersection of software engineering and data science, optimizing large-scale model training and deployment for next-generation applications.
In this role, you will lead architectural decisions that ensure our AI capabilities remain competitive in the rapidly evolving landscape of 2026. If you are passionate about building the backbone of intelligent systems and thrive in a high-performance environment, we want to hear from you.
Why Join Us?
- Work with state-of-the-art Generative AI and Large Language Models.
- Competitive compensation and equity packages.
- Remote-first culture with premium San Francisco amenities.
Responsibilities
- Design and implement high-performance distributed computing clusters for training massive neural networks.
- Optimize model inference latency and throughput using edge computing and GPU acceleration.
- Collaborate with research scientists to translate theoretical models into production-ready software.
- Implement robust CI/CD pipelines for machine learning models to ensure rapid iteration.
- Ensure data security, privacy compliance, and ethical AI standards across all infrastructure.
- Conduct load testing and capacity planning to support scaling demands up to 2026 and beyond.
Qualifications
- 5+ years of experience in software engineering, with a focus on backend systems and machine learning infrastructure.
- Expert proficiency in Python, C++, and CUDA.
- Strong experience with deep learning frameworks (TensorFlow, PyTorch) and orchestration tools (Kubernetes, Docker).
- Familiarity with cloud platforms (AWS, GCP, or Azure) and serverless architectures.
- Experience with vector databases and semantic search technologies.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
- Ability to thrive in a fast-paced, ambiguous startup environment.