Collaborate with data scientists to transform research into production-ready ML solutions. Maintain scalable data pipelines and optimize deployed systems.
Simulmedia is seeking an experienced ML Engineer to join our growing Data Science team and help bridge the gap between research and production. In this role, you will play a key part in turning experimental work into reliable, long-term solutions that deliver value at scale. As an ML Engineer, you will work closely with data scientists and data engineers to transform prototypes, analyses, and models into scalable, maintainable, and efficient production systems.
This position is based in either Kyiv or Lviv, Ukraine, with team members across major cities including Kyiv, Lviv, and Kharkiv. The team primarily works remotely, with occasional in-person meetings in the Kyiv office.
Responsibilities
● Collaborate closely with data scientists to turn research and prototypes into reliable, production-ready solutions.
● Develop and maintain scalable pipelines that power data, feature, and model workflows across the organization.
● Ensure solutions are efficient, reproducible, and maintainable, supporting long-term use and growth.
● Monitor and troubleshoot deployed systems, optimizing performance and reliability over time.
● Apply software engineering best practices, including testing, automation, version control, and deployment processes.
● Contribute to a growing team, helping to improve workflows and support the adoption of data-driven solutions.
Qualifications
● Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
● 7+ years of professional experience in Data Science Engineering, or in software engineering roles with significant exposure to data science workflows.
● Proficiency in Python, with experience developing clean, maintainable, and production-ready code.
● Proficiency in SQL and relational databases, for working with large-scale datasets.
● Solid understanding of software engineering best practices, including version control, testing, and containerization.
● Ability to troubleshoot complex problems, take ownership of solutions, and quickly learn new tools or technologies.
● Strong collaboration and communication skills, able to work effectively with a growing Data Science team.
● Comfortable communicating and coordinating with U.S.-based colleagues.
● Ability to work 11 am - 8 pm EEST.
Bonus points for
● Familiarity with AWS cloud services such as S3, Kubernetes, Lambda, and Redshift for data and ML workflows.
● Data ETL pipelines
● Understanding MLOps practices (model deployment, monitoring, CI/CD).
● Knowledge of machine learning workflows and ability to work with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
