machine learning engineer

The company operates in the biotechnology research sector, focusing on the development of innovative technology solutions. It specializes in building and scaling production-grade machine learning systems to address complex, real-world challenges through the application of modern AI technologies.

• Design, build, and maintain end-to-end machine learning pipelines from data ingestion to deployment and monitoring;
• Deploy, operate, and optimize machine learning models in production environments to ensure reliability, scalability, and performance;
• Develop scalable data pipelines for batch and real-time machine learning workloads;
• Build and improve ML infrastructure, automation, CI/CD processes, and operational tooling;
• Collaborate with data scientists to productionize models and translate research into robust systems;
• Partner with software engineers to integrate ML services into backend platforms and applications;
• Contribute to cloud-native ML platforms using containerization and orchestration technologies;
• Drive continuous improvements to system architecture, observability, performance, and engineering best practices;
• Document workflows, infrastructure, and technical solutions while sharing knowledge across the team.

• Several years of professional experience developing and operating production machine learning systems;
• Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX;
• Experience deploying and managing ML models throughout the complete machine learning lifecycle;
• Strong background in building scalable data pipelines using Apache Spark, Airflow, Prefect, or similar tools;
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform;
• Knowledge of Docker, Kubernetes, CI/CD pipelines, and infrastructure automation;
• Familiarity with distributed systems and designing scalable, reliable software architectures;
• Experience collaborating across multidisciplinary engineering teams;
• Excellent analytical thinking, problem-solving abilities, and communication skills;
• Nice to have: Distributed training, GPU-accelerated machine learning, performance optimization for training or inference, model monitoring, observability, drift detection, streaming or real-time data processing, scientific computing, simulation, data-intensive applications, reinforcement learning, explainable AI, transfer learning.

• Opportunity to work on cutting-edge machine learning solutions with real-world impact;
• Collaborative team of experienced engineers, data scientists, and technical specialists;
• Access to modern cloud-native technologies and industry-leading ML tooling;
• Opportunities for professional development and technical growth;
• Flexible working environment with a strong engineering culture.