In this role, you will play a crucial part in establishing a scalable and efficient pipeline. You will contribute by deploying to Kubernetes clusters and designing the generation pipeline to ensure smooth model implementation.

#LI-DNI

Responsibilities

Design the production-grade generation pipeline
Address specific business problems using machine learning solutions
Handle data collection, cleaning, and preparation for model training
Prepare containerized environments for Kubernetes deployment
Identify and resolve pipeline performance issues
Collaborate with engineers and data scientists to enhance pipeline efficiency and model accuracy
Select appropriate machine learning algorithms to build and train models
Stay informed about new developments in machine learning infrastructure and deployment techniques

Requirements

7+ years of machine learning engineering experience focused on classification metrics
Leadership background in data science initiatives
Background in Python programming and machine learning frameworks like PyTorch and Lightning
Understanding of containerization technologies such as Docker, along with familiarity with Kubernetes
Proficiency in cloud computing platforms like AWS, including S3 and EC2
Experience in Databricks
Strong analytical and problem-solving abilities
Fluency in English at a B2 level or higher

Nice to have

Certifications in machine learning or data science
Expertise in diffusion model architectures
Familiarity with model deployment tools like Weight & Biases and SageMaker Endpoints
Competency in version control systems, including Git
Basic knowledge of model compression techniques