Job Description
At Horizon 2026 Labs, we are not just predicting the future; we are architecting it. We are a cutting-edge research and development firm dedicated to solving humanity's most complex challenges through advanced AI and quantum computing integration. As we approach our 2026 roadmap, we are seeking a visionary Senior AI Systems Engineer to lead our technical infrastructure initiatives.
In this pivotal role, you will be responsible for designing scalable, fault-tolerant machine learning pipelines that can handle petabytes of data in real-time. You will work at the intersection of deep learning and quantum algorithms, pushing the boundaries of what is computationally possible. If you are passionate about building the technological backbone of tomorrow, we want to hear from you.
Responsibilities
- Design, develop, and deploy robust AI models and neural network architectures for high-performance computing environments.
- Lead the integration of quantum-inspired algorithms into classical machine learning workflows to optimize computational efficiency.
- Architect scalable data pipelines using cloud-native technologies (AWS/GCP) to support real-time data ingestion and processing.
- Collaborate closely with cross-functional product teams to define technical requirements and translate business goals into technical roadmaps.
- Mentor junior engineers and conduct rigorous code reviews to maintain high standards of software quality and security.
- Stay abreast of the latest advancements in AI research and evaluate their applicability to Horizon 2026 projects.
Qualifications
- Master’s degree or PhD in Computer Science, Artificial Intelligence, Physics, or a related quantitative field (or equivalent extensive industry experience).
- 7+ years of professional experience in machine learning engineering, with at least 3 years in a leadership or senior architect role.
- Expert proficiency in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Strong experience with cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Demonstrated ability to implement complex algorithms in production environments with a focus on scalability and performance.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.