Job Description
Join Nexus Dynamics as a **Senior AI Strategy & Engineering Lead** to architect the intelligent systems of the future. We are pioneering the next generation of autonomous agents and generative AI solutions designed for the year 2026 and beyond. You will be at the forefront of innovation, working with a world-class team to solve complex problems in scalability, ethical AI, and real-time decision making. This is not just a job; it is a mission to redefine the digital landscape.
Why Join Us?
• Work on cutting-edge Large Language Models (LLMs) and Agentic workflows.
• Competitive equity package and performance bonuses.
• Flexible remote-first culture with state-of-the-art office amenities in the SOMA district.
• Access to the latest hardware (NVIDIA H100 clusters) and proprietary datasets.
Why Join Us?
• Work on cutting-edge Large Language Models (LLMs) and Agentic workflows.
• Competitive equity package and performance bonuses.
• Flexible remote-first culture with state-of-the-art office amenities in the SOMA district.
• Access to the latest hardware (NVIDIA H100 clusters) and proprietary datasets.
Responsibilities
- Lead the design and deployment of scalable AI/ML infrastructure capable of handling petabyte-scale data streams.
- Develop and optimize proprietary Large Language Models for specific industry verticals.
- Define the technical roadmap for AI adoption, ensuring alignment with business goals and 2026 strategic vision.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Collaborate with cross-functional teams including product management, design, and legal to ensure ethical AI implementation.
- Conduct research into emerging neural architectures and generative techniques.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- 8+ years of experience in software engineering, with at least 4 years specializing in AI/ML.
- Deep expertise in Python, PyTorch, TensorFlow, and modern MLOps tools (Kubernetes, MLflow, Airflow).
- Strong understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Proven track record of shipping production-grade machine learning applications.
- Excellent communication skills with the ability to translate technical concepts for non-technical stakeholders.