Job Description
We are pioneering the next generation of intelligent systems for 2026. At Nebula Dynamics, we are building the foundational architecture for Agentic AI—autonomous agents that learn, reason, and execute complex tasks at scale.
If you are a visionary engineer passionate about the future of Artificial General Intelligence (AGI) and want to shape the technology stack of tomorrow, we want to meet you. You will work on cutting-edge projects involving Large Language Models (LLMs), Reinforcement Learning, and Neural Symbolic AI.
Why Join Us?
- Work on mission-critical AI infrastructure used by Fortune 500 companies.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with a focus on deep work.
Responsibilities
- Architect Next-Gen AI Systems: Design and implement scalable machine learning pipelines capable of processing petabytes of data in real-time.
- Optimize LLM Performance: Research and deploy techniques to improve inference speed and reduce latency for large language models.
- Build Autonomous Agents: Develop intelligent agents that can autonomously interact with APIs, databases, and user interfaces to solve complex problems.
- MLOps & Deployment: Establish robust CI/CD pipelines for model training, testing, and deployment using Kubernetes and cloud infrastructure.
- Research & Innovation: Stay at the forefront of AI research, evaluating and integrating new breakthroughs such as Multi-modal learning into our product suite.
- Cross-Functional Leadership: Mentor junior engineers and collaborate with product managers to translate technical possibilities into user-centric features.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent professional experience).
- Core Expertise: Deep understanding of Machine Learning, Deep Learning, and Statistical Modeling.
- Programming: Proficiency in Python, with experience in frameworks such as PyTorch, TensorFlow, or JAX.
- System Design: Strong skills in designing distributed systems, microservices, and high-availability architectures.
- Experience: 5+ years of experience in a software engineering or data science role, with at least 2 years focused on AI/ML.
- Tools: Familiarity with MLOps tools (Docker, Kubernetes, MLflow, Airflow) and cloud platforms (AWS, GCP, or Azure).