Job Description
We are on the precipice of a technological revolution. Nebula Systems is seeking a visionary Senior AI Research Engineer to lead our flagship Project 2026, an initiative dedicated to defining the future of generative artificial intelligence and autonomous systems. If you are a pioneer who thrives in ambiguity and wants to build the next generation of intelligent software, we want to hear from you.
In this role, you will not just implement existing models; you will architect the foundational architectures for our proprietary neural networks. You will work in a high-performance environment that values intellectual curiosity, rigorous experimentation, and real-world impact. Join us in shaping the AI landscape of the coming decade.
Responsibilities
- Architect Scalable AI Systems: Design and implement cutting-edge machine learning models and deep neural networks that power our core product suite.
- Lead Research Initiatives: Spearhead the research and development of novel algorithms for Project 2026, focusing on Large Language Models (LLMs) and multimodal AI.
- Experimentation & Optimization: Conduct rigorous A/B testing and performance benchmarking to optimize model accuracy, latency, and resource efficiency.
- Collaborative Innovation: Partner with cross-functional teams of software engineers, product managers, and data scientists to translate theoretical research into production-ready applications.
- Technical Mentorship: Mentor junior engineers and researchers, fostering a culture of continuous learning and technical excellence within the AI department.
- Publications & Patents: Contribute to the academic community by authoring high-impact research papers and filing patents for proprietary technologies.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field from a top-tier institution.
- Experience: 5+ years of professional experience in machine learning research and engineering, with a strong portfolio of published papers or deployed production models.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with distributed computing frameworks (e.g., Apache Spark, Ray) is highly preferred.
- Domain Expertise: Deep understanding of NLP, Computer Vision, or Reinforcement Learning. Experience with LLMs (GPT, BERT, LLaMA) is essential.
- Problem Solving: Demonstrated ability to tackle complex, open-ended problems and derive scalable solutions from raw data.
- Communication: Exceptional written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.