Job Description
Are you ready to build the infrastructure for the future? Horizon 2026 Dynamics is pioneering the next era of intelligent automation. We are looking for a highly skilled Future Systems Architect to design the neural backbone of our autonomous fleets and predictive analytics platforms.
In this pivotal role, you will bridge the gap between advanced machine learning algorithms and robust physical hardware systems. You will be responsible for the architectural integrity of our core products, ensuring they are scalable, secure, and capable of operating in complex, dynamic environments leading up to and beyond 2026.
Why join us? We offer competitive compensation, equity packages, and the opportunity to work on projects that redefine industry standards.
Responsibilities
- Architectural Design: Design and oversee the implementation of high-performance AI systems and robotics middleware.
- System Integration: Integrate complex neural networks with edge computing hardware for real-time processing.
- Technical Leadership: Mentor a team of senior engineers and data scientists, fostering a culture of innovation.
- Performance Optimization: Continuously refine algorithms to reduce latency and improve decision-making accuracy.
- Future-Proofing: Research emerging technologies (e.g., quantum computing interfaces, neuromorphic chips) to prepare our systems for the technological landscape of 2026.
- Roadmap Development: Define technical roadmaps and architectural standards for product development cycles.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field.
- Experience: 8+ years of experience in software engineering, with at least 4 years specifically focused on AI, Machine Learning, or Robotics architecture.
- Technical Stack: Proficiency in Python, C++, and CUDA. Experience with TensorFlow, PyTorch, or similar deep learning frameworks.
- Domain Knowledge: Deep understanding of computer vision, natural language processing, or reinforcement learning.
- Problem Solving: Demonstrated ability to solve complex, multi-disciplinary engineering problems.
- Communication: Exceptional verbal and written communication skills for cross-functional collaboration.