Job Description
Are you ready to engineer the future? Quantum Leap Systems is seeking a visionary Senior AI/ML Engineer to lead our cutting-edge research and development initiatives for our 2026 product roadmap. We are building the next generation of autonomous systems, and we need a technical leader who thrives on solving complex problems at the intersection of deep learning, NLP, and predictive analytics.
In this role, you will not only implement state-of-the-art algorithms but also define the architectural standards for our AI infrastructure. You will work in a dynamic, fast-paced environment where innovation is encouraged, and your work will directly impact millions of users worldwide.
Why Join Us?
- Competitive salary and equity package.
- Flexible remote-first policy with access to premium tech hubs.
- Professional development budget for conferences and courses.
Don't miss this opportunity to shape the technological landscape of 2026 and beyond.
Responsibilities
- Design, develop, and deploy scalable machine learning models and deep neural networks to solve complex business problems.
- Lead the architectural design of our AI infrastructure, ensuring high availability, scalability, and security.
- Collaborate with product managers and engineers to translate business requirements into technical specifications.
- Conduct research and implement state-of-the-art techniques in Natural Language Processing (NLP) and Computer Vision.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Optimize existing models for performance and reduce inference latency.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related field.
- 5+ years of professional experience in Machine Learning, AI, or Data Science roles.
- Strong proficiency in Python and deep familiarity with frameworks such as PyTorch, TensorFlow, or Keras.
- Proven experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, GCP, or Azure).
- Experience deploying models into production environments using Docker, Kubernetes, and RESTful APIs.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.