Job Description
Are you ready to define the technological landscape of 2026? Nexus Future Systems is on the hunt for a visionary Senior AI/ML Engineer to lead our next-generation autonomous systems division. We are not just building for today; we are architecting the intelligent solutions that will dominate the future of enterprise.
In this pivotal role, you will spearhead the development of next-gen generative models and autonomous agent frameworks. You will work at the intersection of deep learning, scalable architecture, and ethical AI, pushing the boundaries of what is possible. If you are a thought leader who thrives in ambiguity and wants to leave a lasting legacy in the tech industry, we want to hear from you.
Why Join Us?
- Work on cutting-edge Agentic AI technology.
- Competitive equity and compensation package.
- Flexible remote-first policy with premium hubs in SF and NYC.
Responsibilities
- Architect Future-Proof Models: Design and implement scalable machine learning architectures that integrate seamlessly with our 2026 roadmap, focusing on LLMs and reinforcement learning.
- Lead Technical Strategy: Guide a team of elite engineers in optimizing model inference speeds and reducing latency for real-time applications.
- Pioneering Research: Stay at the forefront of AI evolution by researching novel algorithms, including multimodal learning and self-evolving code generation.
- Infrastructure Optimization: Oversee the deployment of models on high-performance GPU clusters and Kubernetes environments, ensuring 99.99% uptime.
- Ethical AI Governance: Establish frameworks for bias mitigation and safety protocols to ensure responsible deployment of autonomous systems.
- Cross-Functional Collaboration: Partner with product managers and data scientists to translate complex research into deployable, user-centric features.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent industry experience).
- Technical Mastery: Extensive experience with Python, PyTorch, TensorFlow, and modern MLOps tools.
- Deep Learning Expertise: Proven track record of deploying state-of-the-art Deep Learning models to production.
- Cloud Proficiency: Strong hands-on experience with AWS, GCP, or Azure, specifically in AI/ML services.
- System Design: Ability to design fault-tolerant, distributed systems capable of handling petabyte-scale data.
- Leadership: Demonstrated ability to mentor junior engineers and lead technical initiatives from conception to delivery.
- Communication: Exceptional ability to explain complex technical concepts to non-technical stakeholders.