Must have

• Machine Learning

• PyTorch

• MLflow

• AWS

• Data pipelines

• ETL

• Quality assurance

• Docker

• English (B2)

Requirements description

What we expect in general:

• 3+ years of hands-on machine learning engineering experience

• Strong proficiency in PyTorch for model development, training, and deployment

• Experience with MLFlow for experiment tracking, model versioning, and lifecycle management

• Practical experience with AWS services

• Proven ability to design, build, and maintain data pipelines for ML workflows

• Experience with data preprocessing, feature engineering, and ETL processes

• Familiarity with data validation and quality assurance practices

• Strong understanding of ML best practices, including reproducibility and versioning

• Experience with containerization (Docker) and orchestration tools

• Familiarity with CI/CD practices for ML systems

• Strong problem-solving skills and attention to detail

• Fluency in English, both written and spoken (at least B2 English level)

Offer description

Project Scope

This project is centered on the critical mission to restore cell health and resilience through cell rejuvenation, ultimately aiming to reverse disease, injury, and age-related disabilities. You will be dedicated to developing generative AI/ML models tailored for multi-modal and multiscale biology. The engineering goal is to create scalable, robust systems that partner with world-class scientists to generate biological insights that lead to the development of novel therapies.

Tech Stack

Python, PyTorch, MLFlow, AWS, ETL

Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements.

What matters most is your passion and willingness to develop. Apply and find out!

A few perks of being with us

• Building tech community
• Flexible hybrid work model
• Home office reimbursement
• Language lessons
• MyBenefit points
• Private healthcare
• Training PackageVirtusity / in-house training
• And a lot more!

Your responsibilities

1. Design and implement efficient training pipelines for machine learning models
2. Configure and execute hyperparameter optimization experiments using Optuna
3. Set up experiment tracking and model registry workflows with MLFlow
4. Manage compute resources and job scheduling on Slurm clusters
5. Build and optimize inference pipelines for model deployment
6. Develop data pipelines to support training and inference workflows

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