Machine Learning Engineer

Company Description Girder AI is an emerging technology company focused on building practical, high-impact artificial intelligence solutions for real-world problems. Based in San Francisco, CA, the team combines strong engineering rigor with modern machine learning research to deliver scalable products and tools. Girder AI values collaboration, continuous learning, and experimentation to drive innovation. Team members are encouraged to take ownership, contribute ideas across the stack, and help shape the company’s technical direction.

Role Description This is a full-time, on-site Machine Learning Engineer role based in San Francisco, CA. The Machine Learning Engineer will design, implement, and optimize machine learning models, with a focus on pattern recognition and neural network architectures. Daily responsibilities include developing algorithms, running experiments, analyzing statistical performance, and iterating on models for deployment in production systems. The role also involves collaborating with product and engineering teams to define requirements, build data pipelines, and ensure model reliability, scalability, and maintainability. The Machine Learning Engineer will write clean, testable code, review peers’ work, and contribute to documentation and best practices.

Qualifications

• Strong foundation in Computer Science, including data structures and Algorithms.
• Applied experience with Neural Networks and Pattern Recognition techniques.
• Solid understanding of Statistics and probabilistic modeling.
• Proficiency in one or more programming languages commonly used in ML (e.g., Python, C++, Java).
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar.
• Familiarity with building and maintaining data pipelines and working with large datasets.
• Bachelor’s or higher degree in Computer Science, Mathematics, Engineering, or a related field, or equivalent practical experience.
• Ability to work collaboratively on-site, communicate clearly, and translate research into production-ready solutions.