Job Description
About Nexus Horizon Technologies
We are at the forefront of the Project 2026 initiative, a revolutionary AI research and deployment program designed to redefine human-machine interaction. We are seeking a visionary Lead AI Engineer to join our elite technical team in Seattle. If you are passionate about building scalable, ethical, and high-performance artificial intelligence systems, this is your opportunity to shape the future.
Why Join Us?
- Work on cutting-edge Generative AI models.
- Competitive compensation and equity packages.
- Flexible remote-first culture with premium Seattle offices.
- Access to top-tier research tools and cloud infrastructure.
Role Overview
As the Lead AI Engineer for Project 2026, you will be responsible for designing the architecture of our next-generation neural networks and overseeing the deployment of large-scale machine learning systems. You will bridge the gap between theoretical research and production-grade software, ensuring our AI solutions are robust, efficient, and scalable.
Responsibilities
- Architect and implement scalable machine learning pipelines and deep learning models for Project 2026.
- Lead a team of data scientists and ML engineers in research, development, and production deployment.
- Optimize existing models for speed, accuracy, and resource efficiency.
- Collaborate with product managers and stakeholders to define AI requirements and success metrics.
- Ensure best practices in data privacy, security, and ethical AI usage.
- Mentor junior engineers and conduct code reviews to maintain high technical standards.
- Stay abreast of the latest research in NLP, Computer Vision, or Reinforcement Learning to integrate novel techniques.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- 8+ years of experience in software engineering, with at least 5 years specifically in machine learning and AI.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Proven track record of deploying large-scale ML models into production environments.
- Experience with MLOps tools and data pipeline frameworks (Airflow, Kubeflow).
- Excellent problem-solving skills and ability to work in a fast-paced, agile environment.