Job Description
Welcome to the vanguard of artificial intelligence. At Nexus Future Labs, we aren't just building software; we are architecting the digital reality of 2026 and beyond. We are seeking a visionary AI Engineer to join our elite engineering team and lead the development of next-generation generative models and autonomous agents.
In this pivotal role, you will push the boundaries of what is possible with Large Language Models (LLMs) and computer vision. You will work in a fast-paced, high-performance environment where your code will directly impact millions of users. If you are passionate about the future of AI and possess the technical grit to build scalable systems, we want to hear from you.
Why join Nexus Future Labs?
- Work on cutting-edge AI infrastructure designed for the 2026 landscape.
- Competitive compensation and equity packages.
- Flexible remote-first culture with premium office amenities in SF.
- Continuous learning budget for conferences and certifications.
Responsibilities
- Architect & Deploy: Design and implement robust machine learning pipelines and production-grade AI models using Python, PyTorch, and TensorFlow.
- Innovation: Pioneer new techniques in Generative AI and Large Language Models to solve complex real-world problems.
- System Optimization: Focus heavily on model inference latency, memory efficiency, and scaling to handle high-volume traffic.
- Collaboration: Partner with product managers, data scientists, and engineers to translate business requirements into technical AI solutions.
- Mentorship: Guide a team of junior engineers and interns, fostering a culture of technical excellence and continuous improvement.
- Research: Stay ahead of the curve regarding 2026 AI trends, publishing papers or contributing to open-source projects where applicable.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related technical field.
- Experience: 4+ years of professional experience in Machine Learning or AI Engineering roles.
- Technical Stack: Deep proficiency in Python, C++, and frameworks such as PyTorch, TensorFlow, or JAX.
- Modeling: Strong understanding of Deep Learning architectures, Transformers, and NLP techniques.
- Tools: Experience with MLOps tools (MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Proven ability to troubleshoot complex system bottlenecks and optimize resource utilization.
- Communication: Excellent verbal and written communication skills to articulate technical concepts to diverse stakeholders.