Nahc1mo ago

Software Engineer (Machine Learning)

Hong KongFull-timemid

Data ScienceSoftware EngineerSoftware Engineering

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Quick Summary

Overview

Our client is a leading creator of body motion video games that run on their own in-house developed device. They are seeking a ML Engineer to to join their highly technical, forward-thinking engineering team focused on foundational technology at the intersection of research and real-world…

Technical Tools

cppdockergojavakubernetespythonpytorchrusttensorflowab-testingmachine-learning

Our client is a leading creator of body motion video games that run on their own in-house developed device.

They are seeking a ML Engineer to to join their highly technical, forward-thinking engineering team focused on foundational technology at the intersection of research and real-world systems, to be part of developing indoor motion tracking technology that makes use of consumer grade hardware.

The ideal candidate will empower researchers and applied scientists by architecting and building mission-critical infrastructure that accelerates ML workflows and model iteration velocity

Lead the design and implementation of training pipelines, automated data workflows, and integration tooling that scale with research demand.

Build systems for large-scale data collection, preprocessing, and curation to support robust experimentation.

Create tools that streamline experiment lifecycles, reduce turnaround time, and help move models toward production smoothly.

Collaborate closely with ML researchers to remove technical blockers and improve developer experience.

Support model serving pipelines and integrate ML components with broader platform systems.

3+ years experience building production-grade machine learning systems, data infrastructure, or research platforms.

Deep hands-on expertise with Python and at least one systems language (e.g., C++, Go, Rust, Java).

Experience working with PyTorch or TensorFlow in production or research environments.

Proven track record with ML training pipelines, data workflows, and integration tooling.

Familiarity with model deployment and inference optimization (MLOps patterns).

GPU-accelerated computing, distributed training systems, data versioning or experiment tracking tools

Docker/Kubernetes exposure