Software Engineer (ML Engineering)

Location: Remote, Singapore, or Hong Kong

Company

hirify.global develops hirify.global Playground, an active play system that uses technology, games, and natural body motion to create social movement experiences for families.

What you will do

• Design and build machine learning training pipelines, data workflows, and model integration systems.
• Develop infrastructure, automation, and shared tools that accelerate research iteration and move experiments toward production.
• Build scalable systems for data collection, curation, preprocessing, and model serving.
• Optimize data pipelines for reliability, performance, and observability.
• Collaborate with ML researchers to remove technical blockers and integrate models with the production framework.
• Write tested code, participate in reviews, document technical decisions, and contribute to cross-role infrastructure projects.

Requirements

• 3+ years of professional software engineering experience building production ML systems, training infrastructure, or research platforms.
• Proficiency in Python and experience with at least one systems language: C++, C#, Java, Rust, or Go.
• Hands-on experience with PyTorch or TensorFlow in production or research environments.
• Experience building or maintaining ML training pipelines or data workflows.
• Familiarity with model deployment, inference optimization, or MLOps practices.

Nice to have

• Experience with distributed training systems or GPU-accelerated computing.
• Knowledge of data versioning, experiment tracking, or ML metadata management.
• Familiarity with Docker and orchestration tools.
• Open-source ML contributions, research publications, or experience in small, high-performance technical teams.
• Background in startups, high-growth environments, or consumer product companies.

Culture & Benefits

• Flexible working hours and vacation policy.
• Product-driven environment focused on individual growth and technical depth.
• Hands-on work with emerging technologies in the gaming field.
• Collaborative team structure with shared ownership of core technology areas.