Job Description
Are you ready to define the technological landscape of 2026?
Nebula Future Tech is seeking a visionary Senior AI Engineer to lead our cutting-edge research division. As we pivot towards the 2026 roadmap, we are building next-generation predictive models that will revolutionize how industries interact with autonomous systems. You won't just be maintaining legacy code; you will be architecting the neural networks of tomorrow.
We are looking for a problem solver who thrives in ambiguity and possesses the technical prowess to turn futuristic concepts into deployable reality. Join us in Austin and help us build the infrastructure for the future.
Responsibilities
- Lead R&D: Spearhead the development of advanced Machine Learning algorithms designed for the 2026 technology horizon.
- System Architecture: Design scalable, fault-tolerant AI infrastructure capable of handling high-throughput data streams in real-time.
- Model Optimization: Refine and optimize large language models (LLMs) and transformer architectures for edge computing environments.
- Collaboration: Work closely with cross-functional teams of data scientists, engineers, and product managers to integrate AI solutions.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Deployment: Oversee the CI/CD pipeline for AI models, ensuring rapid and reliable deployment to production environments.
- Future-proofing: Conduct rigorous research on emerging AI trends to ensure our roadmap remains at the forefront of innovation.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in software engineering and machine learning development.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, or similar ML frameworks.
- Algorithms: Strong understanding of statistical modeling, natural language processing (NLP), and deep learning architectures.
- Cloud: Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to solve complex, unstructured problems with elegant technical solutions.
- Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.