Job Description
We are on the precipice of a technological revolution, and Nexus Future Systems is leading the charge. We are seeking a visionary Senior AI & Machine Learning Engineer to architect the intelligent infrastructure of tomorrow. As we look toward the 2026 era of Artificial General Intelligence, we need a technical expert who can bridge the gap between theoretical research and scalable production systems.
In this role, you will not just write code; you will define the standard for ethical, high-performance AI. You will work in a dynamic environment that prioritizes innovation, autonomy, and the relentless pursuit of excellence.
Why Join Us?
- Work on groundbreaking projects that will shape the next decade of technology.
- Competitive compensation package with equity options.
- Flexible remote-first policy with access to premium co-working spaces in San Francisco.
- Top-tier health, dental, and vision coverage.
Ready to build the future? Apply today.
Responsibilities
- Model Architecture: Design, develop, and optimize cutting-edge deep learning models and algorithms to solve complex, unstructured problems.
- Scalability: Ensure deployed models are scalable, robust, and capable of handling high-throughput real-time data streams.
- Research & Development: Stay at the forefront of the AI landscape, conducting rigorous research on emerging technologies such as LLMs, Transformers, and Neural Architecture Search.
- MLOps Implementation: Build and maintain automated CI/CD pipelines for machine learning, ensuring reproducibility and efficiency in model training and deployment.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineers to translate business requirements into technical AI solutions.
- Code Review: Mentor junior engineers, conduct code reviews, and establish best practices for code quality and documentation.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in building and deploying production-level machine learning systems.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of distributed computing frameworks (Apache Spark, Kubernetes).
- Core Competencies: Deep knowledge of Neural Networks, Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Problem Solving: Exceptional analytical skills with a proven track record of optimizing model performance and reducing latency.
- Communication: Ability to articulate complex technical concepts to non-technical stakeholders clearly and concisely.