Job Description
We are on a mission to redefine the boundaries of artificial intelligence, building scalable, robust systems that power the next generation of intelligent applications. Quantum Leap Innovations is seeking a visionary Senior AI Research Engineer to join our elite team in San Francisco.
In this role, you will bridge the gap between cutting-edge research and production-grade engineering. You will work with a team of world-class scientists and engineers to develop state-of-the-art machine learning models, optimize deep learning pipelines, and deploy AI solutions that have a tangible impact on millions of users.
Join us in shaping the future of technology and solving complex challenges at the intersection of data, scale, and human intelligence.
Responsibilities
- Model Development: Design, implement, and train advanced machine learning and deep learning models using Python, PyTorch, or TensorFlow.
- Optimization: Optimize model inference latency and throughput for production environments, ensuring high performance on edge and cloud devices.
- Research: Conduct in-depth research into novel architectures, algorithms, and training techniques to stay ahead of industry trends.
- MLOps: Build and maintain robust CI/CD pipelines for machine learning, facilitating automated model training, testing, and deployment.
- Cross-Functional Collaboration: Partner with product managers and software engineers to integrate AI capabilities seamlessly into our product ecosystem.
- Code Review: Mentor junior engineers, conduct technical code reviews, and establish best practices for data science and engineering.
Qualifications
- Education: Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python and major deep learning frameworks (PyTorch or TensorFlow).
- Knowledge: Strong understanding of MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and derive insights from large datasets.
- Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.