Job Description
The Future is Now. Chronos Future Systems is pioneering the infrastructure required for the autonomous era of 2026. We are looking for a visionary Senior AI & Quantum Systems Engineer to architect scalable, high-performance computing environments that will power the next generation of predictive AI models.
In this pivotal role, you will bridge the gap between theoretical quantum computing concepts and practical, deployment-ready AI infrastructure. You will lead a team of engineers tasked with optimizing neural network training on non-traditional hardware, ensuring our systems are ready for the massive data loads anticipated by 2026.
If you are passionate about pushing the boundaries of what's possible in software engineering and are looking to define the technical stack of tomorrow, we want to hear from you.
Responsibilities
- Architect Scalable AI Infrastructure: Design and implement robust, distributed systems capable of handling exascale data processing for 2026 deployment targets.
- Optimize Hardware Utilization: Work closely with hardware engineers to fine-tune AI models for quantum processors and next-gen GPU clusters.
- Lead Technical Roadmaps: Define the engineering strategy for the 2026 roadmap, ensuring alignment with business goals and technological feasibility.
- System Reliability: Implement zero-downtime deployment strategies and disaster recovery protocols for critical AI workloads.
- Mentorship: Guide junior developers and data scientists in best practices for large-scale machine learning operations (MLOps).
- Predictive Analytics: Develop and refine algorithms that predict system bottlenecks before they occur.
Qualifications
- Experience: 7+ years of experience in software engineering, with at least 3 years specializing in AI infrastructure or high-performance computing.
- Programming: Deep expertise in Python, C++, and Rust; proficiency in CUDA or OpenCL for GPU acceleration.
- Frameworks: Extensive experience with TensorFlow, PyTorch, and Kubernetes for managing containerized AI workloads.
- Education: BS or MS in Computer Science, Physics, or a related technical field; PhD preferred.
- Problem Solving: Proven track record of solving complex performance bottlenecks in distributed systems.
- Future-Forward Mindset: Demonstrated ability to work with cutting-edge technologies and adapt to rapidly evolving tech stacks.