Chief Machine Learning Engineer

Role Summary

We are seeking a skilled and motivated Machine Learning Engineer to join our team.

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.

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

We offer - Competitive compensation depending on experience and skills

• Variety of projects within one company
• Being a part of a project following engineering excellence standards
• Individual career path and professional growth opportunities
• Internal events and communities
• Flexible work hours