Job Description
Are you ready to architect the future of intelligence? Quantum Leap Technologies is seeking a visionary Senior AI/ML Engineer to spearhead our R&D division dedicated to the 2026 roadmap. In this pivotal role, you will design and deploy next-generation Artificial Intelligence systems that redefine human-machine interaction.
We are not just building software; we are shaping the trajectory of technology for the next decade. You will work in a high-performance environment, collaborating with world-class researchers to transform theoretical breakthroughs into scalable, real-world applications. If you are passionate about pushing the boundaries of Generative AI and Agentic Workflows, this is your chance to lead the charge.
Why Join Us?
- Competitive compensation package (up to $260k).
- Equity stake in a fast-growing unicorn.
- Flexible remote-first policy with premium SF offices.
- Access to cutting-edge GPU clusters and cloud infrastructure.
Ready to define the future? Apply today.
Responsibilities
- Lead the architecture and development of advanced Machine Learning models, focusing on LLMs and Computer Vision for the 2026 product roadmap.
- Design and optimize scalable data pipelines to handle petabyte-scale datasets with high throughput and low latency.
- Collaborate with cross-functional teams (Product, Engineering, Design) to translate complex AI concepts into intuitive user experiences.
- Research and implement novel algorithms to improve model accuracy, efficiency, and interpretability.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
- Ensure the ethical deployment of AI systems, adhering to safety guidelines and bias mitigation strategies.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field (PhD preferred).
- 5+ years of professional experience in AI/ML engineering, with a proven track record of shipping production-grade models.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of Natural Language Processing (NLP) and Large Language Model (LLM) fine-tuning techniques.
- Strong experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Experience with distributed systems and high-performance computing is highly desirable.