About the role

As a Machine Learning Engineer you will build and manage the full lifecycle of machine learning models. Your responsibilities include designing and implementing robust pipelines for data ingestion, model training, deployment, and monitoring. You will use PyTorch for model development, MLFlow for experiment tracking and model versioning, Slurm for job scheduling, and AWS for cloud infrastructure.

• 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

Qualifications

• 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 level)

Project Scope

This project focuses on restoring cell health and resilience through cell rejuvenation, aiming to reverse disease, injury, and age‑related disabilities. You will develop generative AI/ML models for multi‑modal and multi‑scale biology, creating scalable, robust systems in collaboration with world‑class scientists to generate biological insights that lead to novel therapies.

Team

6 members split between San Francisco (SF) and the UK.

What we expect in general

• 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 level)

A few perks of being with us

• Building tech community
• Home office reimbursement
• Training Package
• Virtusity / in‑house training
• And a lot more!

Apply now

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