Job Description
Are you ready to define the technological landscape of the coming decade?
Chronos Future Systems is at the forefront of the Project 2026 initiative—a revolutionary leap in autonomous systems and predictive simulation. We are seeking visionary engineers to architect the neural frameworks that will power the next generation of global infrastructure. This is not just a job; it is a mission to solve humanity's most complex optimization problems.
As a Lead AI Architect, you will be responsible for designing scalable, fault-tolerant machine learning systems that operate in high-stakes environments. You will work with a world-class team of researchers, ethicists, and engineers to push the boundaries of what is possible in AI safety and performance.
Why Join Project 2026?
- Work on cutting-edge technology that will define the future.
- Competitive compensation package with equity options.
- Flexible remote-first culture with access to premium tech stacks.
- Opportunity to publish research and influence industry standards.
Responsibilities
- Lead the architectural design and implementation of core machine learning models for Project 2026.
- Design and optimize distributed training pipelines capable of processing petabytes of real-time data.
- Collaborate with product managers to translate research findings into deployable production systems.
- Establish best practices for code quality, testing, and model monitoring within the engineering department.
- Research and prototype novel algorithms in deep learning and reinforcement learning.
- Mentor junior engineers and contribute to the technical vision of the organization.
- Ensure system scalability, security, and efficiency across global deployment nodes.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in machine learning engineering or applied research.
- Strong proficiency in Python, C++, and distributed computing frameworks (e.g., Apache Spark, Kubernetes).
- Deep understanding of deep learning architectures (Transformers, GNNs) and optimization techniques.
- Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, TensorFlow Extended).
- Proven track record of shipping complex, high-performance software products.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous environment.