Job Description
Are you ready to define the future of artificial intelligence? NovaTech Dynamics is on the cusp of launching Project 2026, a revolutionary initiative aimed at bridging the gap between human cognition and artificial super-intelligence. We are seeking a visionary Senior AI/ML Engineer to lead our core model development team.
In this role, you won't just be writing code; you will architect the neural foundations of a product set to redefine industry standards by 2026. You will work in a high-performance environment with access to cutting-edge compute resources and world-class talent.
Why Join Project 2026?
- Work on a product with a projected valuation of $10B+ upon launch.
- Competitive equity package and top-tier healthcare.
- Flexible remote-first policy with quarterly in-person innovation sprints in San Francisco.
If you are passionate about pushing the boundaries of what is possible in Generative AI and Deep Learning, we want to hear from you.
Responsibilities
- Design and implement scalable deep learning architectures for the Project 2026 neural engine.
- Lead research initiatives to improve model accuracy, latency, and inference speed.
- Mentor junior engineers and data scientists, fostering a culture of innovation and excellence.
- Collaborate with cross-functional teams including hardware engineers and product managers to integrate AI models into real-world applications.
- Stay abreast of the latest advancements in the AI landscape and apply novel techniques to our core systems.
- Optimize existing models for deployment on edge devices and cloud infrastructure.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in machine learning and deep learning engineering.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Proven track record of publishing research or deploying production-level ML models.
- Strong understanding of Large Language Models (LLMs), Transformers, and Reinforcement Learning.
- Experience with distributed training systems (e.g., Ray, Kubernetes, Spark).