Job Description
Join the Architects of Tomorrow.
Nexus Future Labs is a cutting-edge technology firm pioneering the next generation of artificial intelligence. We are looking for a visionary Senior AI Engineer to join our elite team in Seattle. If you are passionate about building scalable, ethical, and robust machine learning systems that will define the landscape of 2026 and beyond, this is your opportunity.
In this pivotal role, you will lead the design and implementation of deep learning architectures, optimize neural networks for real-time inference, and mentor a talented group of engineers. We offer a dynamic environment where innovation is not just encouraged—it is the standard.
What you will do:
Responsibilities
- Architect & Deploy: Design, train, and deploy state-of-the-art machine learning models and deep learning architectures using PyTorch and TensorFlow.
- Optimization: Optimize existing models for high performance, low latency, and scalability in production environments.
- Cross-Functional Collaboration: Partner with data scientists, product managers, and engineers to define technical requirements and drive product roadmaps.
- MLOps: Implement and maintain robust MLOps pipelines to ensure model reproducibility, monitoring, and automated retraining.
- Technical Leadership: Conduct thorough code reviews, provide technical mentorship to junior engineers, and establish best practices for AI development.
- Research Integration: Stay at the forefront of the AI research community, integrating cutting-edge techniques (LLMs, Generative AI) into our core technology stack.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field (or equivalent practical experience).
- Experience: Minimum of 5+ years of professional experience in software engineering and machine learning.
- Technical Skills: Strong proficiency in Python, C++, or Java; deep expertise in PyTorch, TensorFlow, or JAX.
- Domain Knowledge: Proven experience in NLP, Computer Vision, or Reinforcement Learning.
- Cloud & DevOps: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Exceptional analytical skills with a track record of shipping production-ready AI products.