Machine Learning Engineer (Human pose)

Harrison Clarke is an early-stage AI and robotics startup seeking a Machine Learning Engineer to build production-grade machine learning systems. The role involves the full machine learning lifecycle, collaborating with teams to create scalable ML solutions for perception and robotics applications.

Responsibilities:

• Build and maintain end-to-end machine learning pipelines from data preparation through model deployment
• Develop and optimize deep learning models for real-world perception tasks with a focus on performance, scalability, and reliability
• Improve model quality and production systems through experimentation, evaluation, and iterative development
• Partner with research, engineering, and product teams to translate ideas into production-ready machine learning solutions
• Contribute to engineering best practices, system architecture, and the long-term direction of the company's ML platform

Requirements:

• Degree in Computer Science, Machine Learning, Engineering, or a related technical discipline, or equivalent industry experience
• Several years of experience developing and deploying production machine learning systems
• Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch or TensorFlow
• Solid understanding of deep learning, model training, evaluation methodologies, and production ML workflows
• Strong analytical and problem-solving abilities with experience working in collaborative, fast-paced teams
• Advanced degree with research experience in areas such as computer vision, robotics, embodied AI, or machine learning
• Experience building complete ML systems covering data pipelines, model training, inference, and deployment
• Background in perception, multimodal learning, or vision-based machine learning
• Experience with large-scale training infrastructure, experimentation platforms, or production ML environments
• Ability to quickly learn new domains, datasets, and machine learning techniques
• Research publications, open-source contributions, or other evidence of technical impact are beneficial