Job Description
Join Nebula Dynamics, a cutting-edge AI research lab, as our next Senior AI Architect. We are on a mission to redefine the boundaries of artificial intelligence, creating scalable, ethical, and transformative solutions for industries ranging from healthcare to finance. As a key member of our architecture team, you will lead the design and implementation of next-generation machine learning systems.
In this role, you won't just be writing code; you will be architecting the future. You will collaborate with cross-functional teams of data scientists, engineers, and product managers to build robust systems that handle massive datasets and complex algorithms. We value innovation, curiosity, and the drive to push the envelope of what is possible in 2026 and beyond.
Why join us?
We offer a competitive compensation package, equity options, and a fully remote-first culture that prioritizes work-life balance and continuous learning. If you are passionate about building AI that matters, we want to hear from you.
Responsibilities
- Design and architect scalable, high-performance machine learning pipelines and infrastructure.
- Lead technical strategy for AI model deployment, ensuring reliability, latency, and scalability.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Collaborate with stakeholders to translate business requirements into technical architecture.
- Stay abreast of the latest advancements in AI research and integrate them into our product ecosystem.
- Ensure data privacy, security, and ethical AI practices are upheld across all projects.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- 7+ years of experience in software engineering, with at least 4 years focused on AI/ML architecture.
- Expert proficiency in Python, TensorFlow, PyTorch, or equivalent deep learning frameworks.
- Strong understanding of distributed systems, cloud infrastructure (AWS, GCP, or Azure), and containerization (Docker/Kubernetes).
- Proven track record of deploying production-grade ML models at scale.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical audiences.