Job Description
Are you ready to architect the future of intelligence? Nebula Dynamics is seeking a visionary Senior AI/ML Engineer to join our elite team in San Francisco. We are on a mission to build the autonomous systems and generative AI platforms that will define the technological landscape of 2026 and beyond.
In this high-impact role, you won't just be maintaining existing models; you will be pioneering new architectures that push the boundaries of what is possible with Large Language Models (LLMs) and Computer Vision. You will work in a collaborative, fast-paced environment where innovation is encouraged, and your code will directly influence the products used by millions.
Why Join Us?
• Be part of a company that is strictly future-proofing its roadmap.
• Access to cutting-edge hardware and compute resources (NVIDIA H100 clusters).
• Competitive equity package and top-tier healthcare benefits.
• Flexible remote-first hybrid culture.
Responsibilities
- Architect & Deploy: Design, train, and deploy scalable machine learning models and deep learning pipelines capable of handling millions of requests per second.
- Model Optimization: Fine-tune foundation models and optimize inference latency to ensure real-time performance in production environments.
- Research & Development: Stay ahead of the curve by exploring novel AI techniques, including reinforcement learning, federated learning, and multimodal AI.
- Collaboration: Partner with product managers and software engineers to translate complex business requirements into robust, ethical, and accurate AI solutions.
- Infrastructure: Work closely with the DevOps team to implement MLOps best practices, ensuring continuous integration and delivery of model updates.
- Mentorship: Guide junior data scientists and engineers, conducting technical interviews and code reviews to elevate the entire team's standard of excellence.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Statistics, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in machine learning, deep learning, or a related field.
- Programming: Expert proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX.
- Cloud & Infrastructure: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- AI Specialization: Demonstrated experience with Large Language Models (LLMs), prompt engineering, or fine-tuning (LoRA, PEFT).
- Problem Solving: Deep understanding of algorithms, data structures, and statistical analysis with a focus on distributed systems.