Job Description
Welcome to the future of computing. At 2026 Systems, we are not just predicting the next decade; we are engineering it. We are on a mission to define the technological landscape of the year 2026 and beyond, focusing on next-generation Artificial Intelligence, Quantum Computing integration, and autonomous systems.
We are seeking a visionary Lead AI Architect to lead our core infrastructure team. In this role, you will design scalable, high-performance neural networks and optimize deep learning models for real-time inference on next-gen hardware. If you are passionate about pushing the boundaries of what is possible in machine learning and want to build the systems that will power the future, we want to hear from you.
Responsibilities
- Architect & Develop: Design and implement scalable machine learning infrastructure and deep learning pipelines using Python, PyTorch, and TensorFlow.
- Model Optimization: Fine-tune large language models (LLMs) and diffusion models for high-throughput, low-latency inference on specialized hardware (e.g., TPUs, GPUs).
- Research & Innovation: Stay at the forefront of AI research, evaluating new methodologies in Generative AI and Reinforcement Learning to apply to our product suite.
- System Integration: Collaborate with quantum computing researchers to integrate classical AI algorithms with quantum processors.
- Technical Leadership: Mentor junior engineers, conduct code reviews, and establish best practices for AI engineering within the organization.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related field with a focus on Artificial Intelligence.
- Experience: 7+ years of professional experience in machine learning engineering, with at least 3 years in a senior architectural role.
- Technical Skills: Proficiency in Python, C++, and CUDA. Strong experience with distributed training frameworks (Ray, Spark).
- Modeling: Deep understanding of transformer architectures, reinforcement learning, and NLP.
- Problem Solving: Ability to solve complex optimization problems in large-scale systems.