Job Description
Join the Architects of the Future
We are seeking a visionary Senior AI Engineer to spearhead our R&D initiatives targeting the transformative technologies of 2026. At QuantumLeap Dynamics, we are not just building software; we are defining the infrastructure of tomorrow. If you are passionate about Generative AI, Large Language Models, and predictive analytics, and you want to leave a lasting impact on the industry, we want to meet you.
Why Join Us?
- Work on cutting-edge projects that will define the tech landscape in 2026.
- Competitive equity and stock options in a high-growth unicorn.
- Flexible remote-first culture with premium San Francisco office access.
Role Overview
In this pivotal role, you will bridge the gap between theoretical machine learning research and scalable production engineering. You will lead a team of data scientists and engineers to build robust AI systems capable of handling millions of transactions and complex data streams.
Responsibilities
- Design, develop, and deploy scalable machine learning models and AI infrastructure with a focus on future-proofing for 2026 standards.
- Lead the architecture of complex data pipelines and ensure data integrity across distributed systems.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Collaborate with product managers to translate business requirements into innovative technical solutions.
- Research and prototype emerging technologies (e.g., Neuromorphic Computing, Edge AI) to stay ahead of the curve.
- Optimize algorithms for high performance, low latency, and minimal resource consumption.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Minimum of 5+ years of professional experience in AI/ML engineering, preferably in a high-scale environment.
- Strong proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Proven track record of deploying successful AI models into production environments.
- Experience with MLOps, cloud platforms (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Excellent problem-solving skills and the ability to work in a fast-paced, agile environment.