Job Description
Join QuantumLeap Technologies at the forefront of 2026's AI revolution! We're pioneering next-generation machine learning solutions that redefine industry standards. As a Senior AI/ML Engineer, you'll architect scalable neural networks, deploy cutting-edge NLP models, and lead cross-functional teams to transform raw data into actionable business intelligence. Our dynamic startup environment offers unparalleled opportunities to shape the future of artificial intelligence while working alongside world-class researchers and engineers.
We provide comprehensive benefits including equity packages, flexible remote work options, and a $10,000 annual learning stipend. This role requires on-site collaboration in our San Francisco hub 3 days weekly, with the remainder dedicated to flexible remote work.
Responsibilities
- Design and implement production-grade ML pipelines using TensorFlow/PyTorch for high-impact domains
- Lead end-to-end model development from data preprocessing to deployment on cloud platforms (AWS/GCP)
- Mentor junior engineers and conduct peer reviews to maintain code quality standards
- Collaborate with product teams to translate business requirements into technical ML solutions
- Research and integrate emerging AI techniques including quantum computing-optimized algorithms
- Optimize model performance and scalability for real-time inference at petabyte-scale data volumes
- Develop robust MLOps frameworks for continuous model monitoring and retraining
Qualifications
- 5+ years of hands-on experience in ML engineering with proven deployment of production systems
- Expertise in Python, distributed computing (Spark), and containerization (Docker/Kubernetes)
- Strong foundation in mathematics (linear algebra, calculus, statistics) and algorithm design
- Proficiency with cloud ML services (SageMaker, Vertex AI) and infrastructure-as-code tools
- PhD or MS in Computer Science, Mathematics, or related field with research publication record
- Experience leading projects with cross-functional teams in agile environments
- Knowledge of ethical AI frameworks and bias mitigation strategies
- Portfolio demonstrating significant contributions to open-source ML projects