Machine Learning Backend Software Engineer In Medical System (Developer) Middle (Medtech)

TL;DR

Machine Learning Backend Software Engineer (Python/Cuda): Designing and implementing distributed ML systems for a medical platform with an accent on backend services and GPU compute optimization. Focus on deploying computer vision models to production and ensuring system stability under high-load conditions.

Location: Remote

Salary: 250,000–350,000 ₽

Company

Remedy Logic is a medtech company developing advanced medical software and AI systems in collaboration with international scientists and doctors.

What you will do

• Design and implement distributed ML systems and maintain Python backend services.
• Develop and support DB schemas for ML training, validation, and labeling purposes.
• Optimize ML services and increase system reliability through improved unit test coverage and automated testing.
• Deploy research-developed models into the production environment.
• Troubleshoot and resolve issues related to Computer Vision (CV) algorithms in production.
• Investigate error reports and fix bugs to enhance overall system stability.

Requirements

• 2+ years of professional experience in Python development.
• Strong understanding of machine learning algorithms and computer vision.
• Proficiency with GPU compute, Linux, and Docker.
• Experience with SQL (queries, database design, migration tools) and web programming (FastAPI, REST, HTTP, TCP).
• English: B2+ level required.

Nice to have

• Knowledge of medical industry standards such as DICOM and HL7.
• Experience with cloud deployment using GCP, AWS, or Kubernetes (k8s).
• Proficiency in JavaScript.
• Formal education in Computer Science or Data Science.

Culture & Benefits

• Opportunity to work with American and European scientists and doctors.
• Full-time position with flexible working hours.
• Monthly performance bonuses and employee stock options.
• Access to varied and exciting problems with significant growth and learning opportunities.

Hiring process

• Initial screening call.
• Technical interview (approximately 1.5 hours).
• Culture fit interview.