Job Description
Shape the Future of AI in 2026
Are you ready to architect the intelligent systems of tomorrow? Nexus Future Systems is seeking a visionary AI/ML Engineer to join our elite team in San Francisco. We are not just building software; we are defining the trajectory of artificial intelligence for the coming decade.
In this pivotal role, you will spearhead the development of cutting-edge machine learning models and deploy scalable AI solutions that power our next-generation products. You will work at the intersection of data science, software engineering, and strategic product innovation.
Why Join Us?
- Work on projects with a $1B+ valuation potential in the AI sector.
- Access to state-of-the-art hardware and GPU clusters.
- Flexible remote-first culture with a premium tech hub in San Francisco.
Ready to define the technology of 2026? Apply today.
Responsibilities
- Model Development: Design, train, and optimize state-of-the-art deep learning models, including LLMs and generative AI architectures, focusing on performance and accuracy.
- Infrastructure: Build and maintain robust MLOps pipelines using Docker, Kubernetes, and cloud platforms (AWS/GCP) to ensure seamless model deployment and scaling.
- Data Strategy: Collaborate with data engineers to curate high-quality datasets and implement advanced feature engineering techniques.
- Research: Stay at the forefront of the AI landscape, researching emerging papers and technologies to integrate novel approaches into our production systems.
- Optimization: Continuously monitor model performance, conducting A/B testing and iterative improvements to enhance inference speed and reduce latency.
- Collaboration: Partner with cross-functional teams (Product, Design, Engineering) to translate business requirements into technical AI solutions.
Qualifications
- Education: Master’s or Ph.D. degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Programming: Expert-level proficiency in Python, with strong experience in frameworks such as PyTorch, TensorFlow, or JAX.
- Experience: Minimum 4-6 years of professional experience in building and deploying machine learning systems in a production environment.
- Mathematical Foundation: Solid understanding of linear algebra, calculus, probability, and statistics.
- Tools: Familiarity with MLOps tools (MLflow, Airflow) and version control (Git).
- Language: Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.