AI/ML Engineer

About the Role

Join a visionary team focused on creating intelligent systems to interpret and organize human memories enriched with emotional intelligence and contextual awareness. You will spearhead the development of AI-driven capabilities that aid families in organizing and rediscovering their treasured moments.

Key Responsibilities

• Design, develop, and deploy machine learning models specialized in image and video analytics.
• Build natural language processing frameworks to categorize memories effectively.
• Develop recommendation engines aimed at facilitating memory discovery and sharing within families.
• Implement computer vision solutions for tasks such as facial recognition and scene analysis.
• Create AI systems that prioritize privacy and securely manage sensitive familial data.
• Optimize machine learning models to ensure high performance and scalability in production environments.
• Work closely with cross-functional product teams to translate business requirements into AI solutions.
• Maintain awareness of the latest advancements, research, and best practices in AI and machine learning.

Required Qualifications

• Minimum of four years hands-on experience in machine learning engineering.
• Advanced skills in Python programming along with frameworks like TensorFlow, PyTorch, and scikit-learn.
• Proven expertise in computer vision and natural language processing technologies.
• Familiarity with cloud ML platforms such as AWS SageMaker and Google AI Platform.
• Strong understanding of MLOps methodologies and model deployment strategies.
• Experience working with ethical AI standards and strategies to reduce bias in models.
• Solid foundation in mathematics, particularly in statistics and linear algebra.
• Excellent analytical capabilities and problem-solving aptitude.

Preferred Qualifications

• Advanced degree (Master's or PhD) in Machine Learning, Computer Science, or related disciplines.
• Experience with recommendation algorithms, including collaborative filtering techniques.
• Knowledge of edge computing and methods for optimizing models for constrained environments.
• Background in privacy-focused machine learning approaches.
• A track record of published research in reputable AI/ML conferences or scientific journals.