Job Responsibilities:
Leadership & Team Empowerment: Lead, mentor, and manage a diverse, high-performing team of engineers, scientists, and researchers. Foster a culture of collaboration, innovation, and growth within the team.
Cross-Functional Collaboration: Work closely with product, data science, and engineering teams to drive alignment on the AI product roadmap and ensure successful delivery of business outcomes.
AI Model Development & Deployment: Oversee the end-to-end development lifecycle of AI models, including data collection, labeling, model development, experimentation, training, testing, and deployment into production environments.
Best Engineering Practices: Ensure that best practices in coding and engineering are maintained, prioritizing a robust, scalable, and efficient codebase.
Pipeline & Experiment Management: Develop, maintain, and optimize AI model training pipelines and experiment tracking systems. Ensure the seamless flow of data through the system and timely feedback on model performance.
Operational Excellence: Manage the deployment, scaling, and maintenance of AI models in production, working to ensure the systems are reliable, scalable, and continuously optimized.
Required Qualifications:
3+ years of experience in managing teams of engineers, scientists, or researchers, with a strong track record of driving successful AI projects.
Hands-on experience in designing, implementing, and deploying machine learning models in production environments, with a focus on business applications.
Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and business leaders.
Proficiency in modern software engineering practices, including writing clean, maintainable, and well-documented code.
Desired Qualifications:
Practical knowledge of MLOps principles and technologies (e.g., model versioning, deployment at scale, containerization, continuous integration/delivery).
Familiarity with cloud platforms (e.g., AWS, Google Cloud) and container orchestration tools (e.g., Kubernetes, Docker).
Experience with Python (e.g., FastAPI, PyTorch, and related libraries), and modern database systems (e.g., SQL, NoSQL).
Experience in building and optimizing large-scale data pipelines and AI model training systems.
Knowledge of the Japanese language is a plus.
Team Culture & Expectations:
Collaboration: We emphasize teamwork, and engineers are expected to collaborate effectively across functions, sharing knowledge, and empowering each other.
Ownership & Accountability: Engineers are encouraged to take ownership of their projects, ensuring successful delivery while being proactive about resolving challenges.
Innovation & Experimentation: Engineers have the opportunity to experiment with new technologies and ideas, contributing to continuous innovation and improvements in our AI models and products.
Quality: We maintain a high standard of code quality, always striving for excellence and continuous improvement. Engineers are expected to deliver reliable, high-performance solutions.
Customer-Focused: We value both internal and external customers. Engineers should prioritize delivering value to end-users while also supporting colleagues and contributing to the broader company mission.