Job Description
We are QuantumCore Dynamics, a pioneering force in autonomous systems and generative AI. We are currently seeking a visionary Lead AI Architect (2026 Vision) to join our elite engineering team in Seattle. In this role, you will not just build software; you will architect the future of machine learning, ensuring our solutions remain at the cutting edge of technology.
As we scale towards our 2026 roadmap, we need a leader who can bridge the gap between theoretical research and production-grade infrastructure. You will be responsible for designing scalable neural networks, optimizing deep learning models, and establishing technical standards that will define our product suite for years to come.
Why Join Us?
- Competitive salary and equity package.
- Comprehensive health benefits and 401(k) matching.
- Flexible remote/hybrid work environment.
- Access to the latest hardware and cloud resources.
Responsibilities
- Define the technical vision and architecture for our core AI products leading up to 2026.
- Design and deploy large-scale deep learning models with a focus on efficiency and accuracy.
- Collaborate with cross-functional teams to integrate AI capabilities into consumer and enterprise applications.
- Mentor and guide junior data scientists and software engineers to foster a culture of innovation.
- Conduct rigorous code reviews and establish best practices for data pipelines and model deployment.
- Research and evaluate emerging technologies (e.g., Transformer architectures, reinforcement learning) to keep our stack competitive.
- Optimize existing models for latency and cost reduction in high-traffic environments.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 7+ years of experience in software engineering, with at least 4 years focused on AI/ML architecture.
- Expert proficiency in Python, TensorFlow, PyTorch, or JAX.
- Strong understanding of distributed systems, cloud architecture (AWS/Azure/GCP), and containerization (Docker/Kubernetes).
- Proven track record of deploying production-ready machine learning models at scale.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
- Experience with MLOps and model versioning tools (e.g., MLflow, Kubeflow).