Lead Machine Learning Engineer

We are seeking a skilled and motivated Machine Learning Engineer to join our team.
In this role, you will be instrumental in building a robust and efficient pipeline. You will take on responsibilities such as deploying to the Kubernetes cluster and designing the generation pipeline to ensure seamless model execution.

#LI-DNI

Responsibilities

• Design and implement the production generation pipeline
• Identify business problems addressable with machine learning
• Collect, clean, and prepare data for model training
• Prepare containers for Kubernetes deployment
• Monitor and troubleshoot the pipeline for any errors or performance issues
• Collaborate with other engineers and data scientists to optimize the pipeline and improve model performance
• Select suitable machine learning algorithms, build, and train models
• Stay updated with the latest advancements in machine learning infrastructure and deployment technologies

• 5+ years of experience in machine learning engineering with a focus on classification metrics for machine learning
• Leadership experience in data science projects
• Background in Python programming and machine learning libraries like PyTorch and Lightning
• Understanding of containerization technologies like Docker and familiarity with Kubernetes
• Proficiency in cloud platforms like Amazon Web Services, specifically S3 and EC2 instances
• Experience in Databricks
• Analytical and problem-solving mindset
• Fluency in English at B2 level or higher

• Certifications in machine learning or data science
• Knowledge of diffusion models architecture
• Familiarity with model deployment tools like Weight & Biases and SageMaker Endpoints
• Experience with Git and version control systems
• Basic understanding of model compression techniques

• 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