Software Engineer, ML Engineering
nex • Hong Kong • Hong Kong or Remote
Posted: June 25, 2026
Job Description
Location: Remote, or Hong Kong based
Type: Full Time
The Role
As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: training pipelines, data workflows, model integration systems, and the tools that enable rapid experimentation. Your work ensures researchers can iterate reliably and move experiments toward production readiness.
The role offers the opportunity to work on deeply technical problems in machine learning systems, data infrastructure, sensing technologies, and real-time inference. You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas.
The Mindset
You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in ML systems, distributed computing, or real-time software directly shapes what the platform can do.
What You’ll Do
- Design and build training pipelines, data workflows, and model integration systems
- Develop infrastructure that accelerates research iteration and reduces turnaround time
- Build systems for data collection, curation, and preprocessing at scale
- Create tools and automation that move experiments toward production readiness
- Optimize data pipelines for reliability, performance, and observability
- Collaborate with ML researchers to understand their needs and remove technical blockers
- Work on model serving infrastructure and integration with the production framework
- Write clean, well-tested code that maintains high engineering standards
- Participate in code reviews and help raise the engineering bar across the team
- Contribute to shared tools, infrastructure, and cross-role projects (20% Time)
- Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction
- Document systems and decisions to support team knowledge sharing
Must Have
- 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms
- Proficiency in Python, additional experience with at least one other 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 containerization (Docker) and orchestration tools
- Contributions to open-source ML projects or research publications
- Experience working in small, high-performance technical teams
- Background in startups, high-growth environments, or consumer product companies
- Passion for pushing technical boundaries and deep problem-solving
Additional Content
Location: Remote, or Hong Kong based
Type: Full Time
The Role
As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: training pipelines, data workflows, model integration systems, and the tools that enable rapid experimentation. Your work ensures researchers can iterate reliably and move experiments toward production readiness.
The role offers the opportunity to work on deeply technical problems in machine learning systems, data infrastructure, sensing technologies, and real-time inference. You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas.
The Mindset
You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in ML systems, distributed computing, or real-time software directly shapes what the platform can do.
What You’ll Do
- Design and build training pipelines, data workflows, and model integration systems
- Develop infrastructure that accelerates research iteration and reduces turnaround time
- Build systems for data collection, curation, and preprocessing at scale
- Create tools and automation that move experiments toward production readiness
- Optimize data pipelines for reliability, performance, and observability
- Collaborate with ML researchers to understand their needs and remove technical blockers
- Work on model serving infrastructure and integration with the production framework
- Write clean, well-tested code that maintains high engineering standards
- Participate in code reviews and help raise the engineering bar across the team
- Contribute to shared tools, infrastructure, and cross-role projects (20% Time)
- Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction
- Document systems and decisions to support team knowledge sharing
Must Have
- 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms
- Proficiency in Python, additional experience with at least one other 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 containerization (Docker) and orchestration tools
- Contributions to open-source ML projects or research publications
- Experience working in small, high-performance technical teams
- Background in startups, high-growth environments, or consumer product companies
- Passion for pushing technical boundaries and deep problem-solving