Job Description
Are you ready to shape the future of technology in 2026?
Nexus Innovations is on the hunt for a visionary Advanced AI & Machine Learning Engineer to join our elite engineering team in San Francisco. As we prepare to launch our next-generation autonomous systems, we need a technical mastermind who thrives in ambiguity and pushes the boundaries of what is possible.
In this role, you won't just be maintaining existing models; you will architect the foundational algorithms that will power the autonomous economy. We are looking for a builder who understands the nuances of Generative AI, Reinforcement Learning, and Large Language Models.
Why Join Us?
- Work on cutting-edge technology that defines the 2026 tech landscape.
- Competitive equity package and salary.
- Flexible remote-first culture with state-of-the-art office amenities.
- Unlimited PTO and continuous learning budget.
Responsibilities
- Architect & Train Models: Design, develop, and train state-of-the-art Deep Learning and Machine Learning models using Python, PyTorch, and TensorFlow.
- GenAI Leadership: Spearhead research and implementation of Generative AI solutions, focusing on LLM fine-tuning and prompt engineering strategies.
- System Optimization: Optimize model inference latency and scalability for high-volume production environments.
- R&D Collaboration: Partner with cross-functional teams (Product, Research, and Engineering) to translate complex business requirements into technical AI solutions.
- Deployment: Manage the end-to-end deployment of ML models on cloud infrastructure (AWS/GCP) and edge devices.
- Performance Monitoring: Establish robust monitoring systems to track model performance, accuracy drift, and system health.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field (or equivalent practical experience).
- Programming: Expert proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, JAX).
- Experience: 4+ years of professional experience in Machine Learning or AI engineering roles.
- Specialized Knowledge: Deep understanding of NLP, Computer Vision, or Reinforcement Learning.
- Tools: Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms.
- Communication: Excellent verbal and written communication skills to articulate complex technical concepts to non-technical stakeholders.