Python Software Engineer (ML Infrastructure)

Your responsibilities

• Design, develop, maintain, and optimize high-quality Python libraries and services used across multiple projects.
• Build reusable, well-documented Python packages and ensure reliable versioning, publishing, and dependency management.
• Implement and maintain robust testing frameworks, including unit, integration, and performance tests.
• Develop and enhance CI/CD pipelines using GitHub Actions and other automation tools.
• Collaborate with software engineers, data scientists, and ML researchers to support machine learning workflows and production deployments.
• Monitor application performance, conduct profiling, and implement optimizations for scalability and reliability.
• Support deployment and operational activities on AWS cloud infrastructure.
• Contribute to architecture discussions, code reviews, and engineering best practices.
• Assist in deploying and managing GPU-enabled workloads for machine learning applications.
• Maintain technical documentation and provide troubleshooting support when required.

We are looking for you, if you have

• Deep understanding of the Python ecosystem, including packaging, dependency management, static typing, and modern tooling such as uv, mypy, and ruff.
• Strong experience building, publishing, and maintaining Python packages consumed by internal teams or external users.
• Hands-on experience with software testing practices, including unit testing, integration testing, and performance profiling.
• Experience designing and maintaining CI/CD pipelines, preferably using GitHub Actions.
• Experience working with cloud infrastructure, preferably AWS services and deployment environments.
• Ability to write clean, maintainable, and production-grade code with a strong focus on code quality and developer experience.

Technology Stack

Python, Packaging, Static Typing, uv, mypy, ruff, CI/CD, GitHub Actions, AWS, PyTorch, spaCy, Testing Frameworks, Performance Profiling

Nice to have

• Familiarity with machine learning frameworks such as PyTorch, spaCy, or similar ecosystems.
• Understanding of GPU-accelerated workloads and deployment considerations for ML models.
• Exposure to speech technologies, including Automatic Speech Recognition (ASR), phoneme recognition, and forced alignment systems.
• Experience contributing to open-source projects.
• Knowledge of Docker, Kubernetes, and Infrastructure-as-Code tools.
• Familiarity with observability, monitoring, and performance optimization practices.

We offer

• Participation in interesting and demanding projects.
• Flexible working hours.
• A great, non-corporate atmosphere.
• Stable employment conditions: contract of employment or B2B contract.
• Opportunities for development and promotion.
• Attractive package of benefits.
• Remote or hybrid work model, with 2 days per week from the office.

We reserve the right to contact the selected candidates.