Machine Learning Engineer II - Content Intelligence Team

data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. Were the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make. Through our products, partners and people, we make it easier for everyone to experience the world.

Leadership/Team Quote:
The Content Intelligence team builds the Content Intelligence Platform by consuming millions of images and textual inputs every day, and then enriching it with ML capabilities. Eventually, these will serve downstream applications and personalize our customers' experience (think of a way to choose and surface the right images and reviews when customers book their next vacation).
Moreover the team is taking a key role in the new GenAI project - The AI Trip Planner.

Role Description:
Youll work with top notch engineers and data scientists from the team on bringing it to the next level and enabling optimal user experience. The work will focus on building, training and deploying content models (Computer vision, NLP and Generative AI) using the most advanced technologies and models.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.