Job Description
We are looking for a visionary Senior Machine Learning Engineer to join our elite R&D team in San Francisco. As we prepare for the technological landscape of 2026, QuantumLeap is building the next generation of generative AI and predictive analytics tools. This is a unique opportunity to shape the future of industry-standard AI solutions.
Why Join Us?
At QuantumLeap, we don't just adapt to the future; we invent it. You will work with a world-class team of data scientists, engineers, and product leaders to deploy scalable models that impact millions of users. We offer a competitive salary, comprehensive benefits, and the freedom to innovate.
What You Will Do:
- Design and implement state-of-the-art machine learning models and deep learning architectures.
- Lead the end-to-end machine learning lifecycle, from data ingestion and feature engineering to model deployment and monitoring.
- Collaborate with cross-functional teams to translate complex business requirements into technical AI solutions.
- Optimize existing algorithms for speed, accuracy, and scalability in production environments.
- Conduct cutting-edge research to stay ahead of emerging trends in AI, such as Large Language Models (LLMs) and multimodal learning.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Ensure model robustness, interpretability, and compliance with data privacy regulations.
Qualifications:
- PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
- 7+ years of professional experience in machine learning, deep learning, or data science.
- Proficiency in programming languages such as Python, PyTorch, or TensorFlow.
- Strong experience with distributed computing frameworks (e.g., Apache Spark, Kubernetes) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of deploying production-grade machine learning models that drive measurable business impact.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Experience with MLOps tools and CI/CD pipelines is highly desirable.
Responsibilities
- Design and implement state-of-the-art machine learning models and deep learning architectures.
- Lead the end-to-end machine learning lifecycle, from data ingestion and feature engineering to model deployment and monitoring.
- Collaborate with cross-functional teams to translate complex business requirements into technical AI solutions.
- Optimize existing algorithms for speed, accuracy, and scalability in production environments.
- Conduct cutting-edge research to stay ahead of emerging trends in AI, such as Large Language Models (LLMs) and multimodal learning.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Ensure model robustness, interpretability, and compliance with data privacy regulations.
Qualifications
- PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
- 7+ years of professional experience in machine learning, deep learning, or data science.
- Proficiency in programming languages such as Python, PyTorch, or TensorFlow.
- Strong experience with distributed computing frameworks (e.g., Apache Spark, Kubernetes) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of deploying production-grade machine learning models that drive measurable business impact.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Experience with MLOps tools and CI/CD pipelines is highly desirable.