Machine Learning Engineer, Metropolis

• MS or PhD in Computer Science, Engineering, or a related field, or equivalent work experience
• 3+ years of experience in machine learning and software engineering, with expertise in computer vision and multi-modal models
• Proficiency in Python and Machine Learning frameworks (PyTorch/TensorFlow/ONNX/TensorRT)
• Strong experience with cloud services, preferably AWS
• Familiarity with Machine Learning model serving infrastructure and best practices
• Knowledge of transactional and analytical database technologies
• Strong understanding of the agile development process and CI/CD pipelines and tools (e.g., Github Actions, Jenkins)
• Excellent communication skills, capable of presenting complex technical information clearly

Desirable

• As a Machine Learning Engineer at Metropolis, you will accelerate recognition commerce through data innovation. You will be a key leader in Engineering, responsible for designing and building the robust generative data engine and intelligent algorithms that fuel our ML models
• Your mission is to bridge data and models through automation, creating the scalable foundation that underpins the predictive, personalized experiences defining the future of the built world
• Work with the machine learning team to perform state-of-the-art research, train, and optimize machine learning algorithms
• Design, implement, and maintain active learning algorithms and generative vision models to ensure cost efficient data sampling and learning
• Maintain and revisit innovative methodologies to curate high quality data and annotations
• Design, implement, and maintain the machine learning data pipelines and infrastructure
• Work with technologies like AWS, SQL/NoSQL databases, Kafka, Airflow, and NVIDIA Triton, ensuring efficient and effective use of these tools
• Collaborate with other Engineering and Product teams to define requirements and facilitate the smooth deployment of software into production
• Play a vital role in the entire development lifecycle, from concept and requirement identification to implementation
• Implement best practices in automation, testing, and deployment to foster rapid feature/model development and release
• Foster a culture that views bugs and production issues as opportunities for improvement
• Contribute to the hiring and cultural development within Metropolis Engineering
• Ensure high availability and scalability of Machine Learning systems
• Collaborate with partners to integrate their products seamlessly into the Metropolis platform