Company Description

MentalHelp.Global is a non-profit organization dedicated to providing open-source AI solutions for mental health support worldwide. Our mission is to complement traditional therapeutic care by addressing accessibility gaps, especially in underserved areas, and offering assistance in any language. We prioritize security and transparency, with localized servers and a commitment to tracking impact for researchers. Our efforts are guided by a collaborative, global team and partnerships with local universities to ensure inclusivity and relevance. The organization is driven by a vision to foster community resilience and improve mental health outcomes globally.

Role Description

This is a full-time, hybrid role based in Kyiv City, Ukraine, with flexibility for some work-from-home arrangements. As a Machine Learning Engineer, you will be responsible for designing, developing, and optimizing AI models to address mental health challenges. Daily tasks include working on neural network architectures, implementing advanced algorithms, analyzing data for pattern recognition, and collaborating with teams to refine solutions. You will contribute to foundational research, model evaluation, and the development of scalable AI systems to support the organization’s mission.

Key Responsibilities

• Design, build, and optimize ML pipelines, including data ingestion, feature engineering, training, and deployment for large-scale, real-time systems.
• Improve and extend core ML Platform capabilities such as the feature store, model registry, and embedding-based retrieval services.
• Collaborate with product software engineers to integrate ML models into user-facing features like recommendations, personalization.
• Conduct model experimentation, A/B testing, and performance analysis to guide production deployment.
• Optimize and refactor existing systems for performance, scalability, and reliability.
• Ensure data accuracy, integrity, and quality through automated validation and monitoring.
• Participate in code reviews and uphold engineering best practices.
• Manage and maintain ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring.

Requirements

• 3+ years of experience as a professional software or machine learning engineer.
• Proficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).
• Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark, Databricks, or similar.
• Experience working with systems at scale and deploying to production environments.
• Cloud experience (AWS, Azure, or GCP), including building, deploying, and optimizing solutions with ECS, EKS, or AWS Lambda.
• Strong understanding of ML model trade-offs, scaling considerations, and performance optimization.
• Bachelor’s in Computer Science or equivalent professional experience.

Nice to Have

• Experience with embedding-based retrieval, recommendation systems, ranking models, or large language model integration.
• Experience with feature stores, model serving & monitoring platforms, and experimentation systems.
• Familiarity with large-scale system design for ML.