Senior Machine Learning Engineer

Our team is partnering with a cutting-edge medtech company in Los Angeles to fill a key role on their AI and machine learning team. They are seeking a highly skilled Senior Machine Learning Engineer with expertise in generative AI and computer vision to lead the development and deployment of advanced models for medical imaging solutions. In this role, you will design end-to-end machine learning pipelines, optimize models for clinical standards, and collaborate closely with cross-functional teams to ensure seamless integration into production environments.

Requirements of the Senior Machine Learning Engineer:

• BS in Computer Science, Engineering, or related field (Master's preferred).
• 8+ years in machine learning with a focus on deploying models in production; experience with foundation models like Stable Diffusion, SAM, DINO, and Vision Transformers.
• Strong proficiency in Python, C++, or Java, and familiarity with development best practices (version control, CI/CD, testing).
• Expertise in frameworks like TensorFlow, PyTorch, and OpenCV; experience deploying models at scale.
• Experience with MLflow, Kubeflow, or Apache Airflow.
• Skilled in computer vision algorithms (segmentation, detection, generation).
• Experience with AWS, GCP, or Azure, and containerization (Docker, Kubernetes).
• Skilled in large-scale dataset management, preprocessing, and augmentation.
• Strong collaboration skills, especially in fast-paced, cross-functional environments.
• Excellent analytical and problem-solving skills in computer vision and AI.
• Experience with tools like Prometheus, Grafana, or ELK Stack.
• Experience in healthcare or medical imaging is a plus.

Responsibilities for the Senior Machine Learning Engineer:

• Build and enhance generative AI and computer vision models for improved image analysis.
• Write scalable, maintainable, high-quality code for seamless integration of models into production environments.
• Design and maintain end-to-end machine learning pipelines, ensuring efficiency and scalability.
• Partner with R&D, product, and regulatory teams to align models with clinical and regulatory standards.
• Optimize and fine-tune models for performance, accuracy, and clinical standards.
• Work with software engineering teams to integrate ML models into broader platforms.
• Keep detailed documentation of processes and ensure adherence to regulatory requirements.
• Stay current with advancements in AI, applying innovations to improve company products.