Job Description
About 2026 Dynamics:
We are at the forefront of the next industrial revolution. Our mission is to integrate quantum-ready AI architectures into enterprise workflows, creating systems that are not just intelligent, but adaptive and future-proof. We are looking for a visionary Senior AI Architect to join our elite engineering team in San Francisco.
The Role:
As a Senior AI Architect at 2026 Dynamics, you will be responsible for designing the blueprint of our next-generation intelligence systems. You will bridge the gap between theoretical machine learning research and scalable production engineering, ensuring our solutions are robust, secure, and industry-leading.
Why Join Us?
⢠Work with cutting-edge technology that defines the future.
⢠Competitive compensation package with equity options.
⢠Flexible remote and hybrid work culture.
⢠Professional development in a fast-paced, innovative environment.
Responsibilities
- Design and architect scalable, high-performance AI/ML infrastructure for large-scale deployments.
- Lead the research and implementation of novel machine learning algorithms, with a focus on efficiency and accuracy.
- Collaborate with cross-functional teamsâincluding data scientists, software engineers, and product managersâto define technical requirements and solutions.
- Oversee the full software development lifecycle (SDLC) for AI initiatives, from prototyping to production.
- Mentor and guide junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Ensure system reliability, security, and compliance with industry standards.
- Stay abreast of emerging trends in AI, quantum computing, and distributed systems.
Qualifications
- PhD or Masterâs degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 7 years of experience in software engineering, with at least 4 years specifically focused on AI/ML architecture.
- Deep expertise in Python, PyTorch, TensorFlow, or similar ML frameworks.
- Strong understanding of distributed systems, cloud platforms (AWS, GCP, or Azure), and containerization (Docker, Kubernetes).
- Proven track record of deploying production-grade machine learning models.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Experience with MLOps and model lifecycle management tools.