Machine Learning Engineer (AWS)

Machine Learning Engineer (AWS)

We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization.

About the role

As a Machine Learing Engineer your main challenge is to build and manage the entire lifecycle of machine learning models. This involves creating robust pipelines that handle everything from data input to model deployment. You will use PyTorch to develop models and then use MLFlow to track every experiment and manage model versions. You will also be responsible for managing our computational resources on Slurm and keeping ML infrastructure running smoothly on AWS.

Project

Nexyra

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

Challenges

• Design and implement efficient training pipelines for machine learning models

• Configure and execute hyperparameter optimization experiments using Optuna

• Set up experiment tracking and model registry workflows with MLFlow

• Manage compute resources and job scheduling on Slurm clusters

• Build and optimize inference pipelines for model deployment

• Develop data pipelines to support training and inference workflows

• Deploy and maintain ML infrastructure on AWS

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)

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 Package

• Virtusity / in-house training

And a lot more!