Job Description
We are on the bleeding edge of innovation. Nexus Future Systems is seeking a visionary Senior AI & Machine Learning Engineer to architect the next generation of artificial intelligence. As we look toward the future of 2026, we need a leader who can bridge the gap between theoretical breakthroughs and scalable production solutions.
In this role, you will not just write code; you will define the ethical standards and technical architecture for the AI-driven world of tomorrow. You will work alongside a world-class team of researchers and engineers to develop AGI-ready models and next-generation neural networks.
Why join us?
• Work on cutting-edge generative AI and large language models.
• Competitive equity package and salary.
• Flexible remote-first culture with premium office amenities in SF.
Responsibilities
- Architect Next-Gen Models: Design and implement scalable deep learning architectures capable of handling complex, real-world 2026-era datasets.
- Optimize Performance: Push the boundaries of inference speed and model accuracy using advanced quantization and pruning techniques.
- Ethical AI Oversight: Ensure all AI systems adhere to rigorous ethical guidelines, bias mitigation protocols, and safety standards.
- Research & Development: Stay at the forefront of AI trends, experimenting with new paradigms like Transformer extensions and reinforcement learning.
- Team Leadership: Mentor junior engineers and conduct code reviews to maintain high engineering standards across the department.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
- Technical Expertise: Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- Experience: 5+ years of experience in machine learning engineering, specifically in Natural Language Processing (NLP) or Computer Vision.
- Architecture: Proven track record of deploying large-scale ML models into production environments.
- Problem Solving: Exceptional ability to debug complex system issues and optimize resource utilization.