Job Description
Join the Visionaries of 2026
Quantum Leap Systems is on a mission to redefine the technological landscape of the future. We are looking for a visionary Senior Machine Learning Engineer to lead our R&D efforts in preparing our infrastructure for the 2026 roadmap. You will not just be building models; you will be architecting the intelligent systems that will power our products for the next decade.
In this pivotal role, you will bridge the gap between theoretical AI research and production-scale deployment. You will work with a world-class team of engineers and data scientists to pioneer solutions in predictive analytics, generative AI, and autonomous decision-making systems.
Why Join Us?
* Shape the Future: Directly influence the technology stack that will define 2026.
* Competitive Compensation: Top-tier salary and equity package.
* Cutting-Edge Tech: Work with state-of-the-art frameworks and cloud infrastructure.
* Flexible Culture: Remote-first with a vibrant office culture in the heart of Seattle.
Responsibilities
- Lead the end-to-end development of scalable machine learning models and pipelines for the 2026 product roadmap.
- Architect robust data pipelines to ingest, process, and analyze massive datasets in real-time.
- Collaborate with cross-functional teams (Product, Engineering, Design) to translate business requirements into technical AI solutions.
- Optimize model performance, accuracy, and inference speed to ensure seamless user experiences.
- Stay at the forefront of AI research, evaluating and integrating emerging technologies (e.g., LLMs, reinforcement learning) into our ecosystem.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
- Minimum of 5 years of professional experience in Machine Learning, Data Science, or AI Engineering.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Deep experience with distributed computing systems (e.g., Spark, Hadoop) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of deploying production-grade machine learning models.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous environment.