Job Description
Are you ready to shape the future of Artificial Intelligence? Nexus Future Systems is seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. We are building the next generation of generative models and scalable machine learning infrastructure that will define the industry standard for 2024 and beyond.
In this role, you will not just write code; you will architect the intelligence that powers our products. You will work at the intersection of data science and software engineering, pushing the boundaries of what's possible with Large Language Models (LLMs) and neural networks. If you are passionate about ethical AI, high-performance computing, and solving complex problems, we want to hear from you.
Why Join Us?
- Impactful Work: Your models will be used by millions, driving innovation across multiple sectors.
- Top-Tier Talent: Collaborate with Ph.D.-level researchers and industry veterans.
- Modern Stack: Access to the latest GPUs, cloud infrastructure, and development tools.
Responsibilities
- Design, develop, and deploy state-of-the-art machine learning models and algorithms at scale.
- Optimize model inference performance to reduce latency and increase throughput for real-time applications.
- Collaborate closely with data scientists and product managers to translate business requirements into technical solutions.
- Implement rigorous testing, validation, and monitoring strategies to ensure model reliability and accuracy.
- Contribute to the open-source community and stay ahead of the curve on emerging AI research.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- Master’s or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- 5+ years of professional experience in machine learning, deep learning, or AI engineering.
- Strong proficiency in Python and deep understanding of machine learning frameworks (TensorFlow, PyTorch, or JAX).
- Experience deploying models to production environments (AWS, GCP, or Azure).
- Familiarity with MLOps practices, including CI/CD pipelines for ML and model versioning.
- Excellent communication skills and the ability to explain complex technical concepts to non-technical stakeholders.