Machine Learning Engineer

Confidential Jobs; Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

Confidential Jobs is seeking a Machine Learning Engineer to own the productionisation of computer vision models, bridging model science and software engineering. You will deploy and optimize CV models across cloud and edge environments, while ensuring reliability and scalable serving architectures.

The role requires hands-on experience with ML systems, model versioning, and collaboration with cross-functional teams to deliver production-grade solutions.

Qualifications

• Bachelor’s or Master’s in Computer Science, Electrical Engineering, Machine Learning, or related field.
• 4+ years of experience in machine learning engineering, with production deployments to your name.

Responsibilities

• Deploy and maintain model serving infrastructure across cloud and edge environments.
• Optimise models for deployment: TensorRT, INT8/FP16, and edge ensemble fusion.
• Validate latency/throughput trade-offs and production rollout criteria.
• Manage model versioning and coordinated rollout across cloud and edge devices.
• Build end-to-end inference pipelines from input to structured output.
• Ensure inference reliability and graceful degradation in field conditions.
• Define integration contracts for downstream software systems.
• Design and maintain evaluation frameworks and active learning pipelines.
• Collaborate with CV Scientists on handoff requirements and with Software Engineers on API integration.
• Communicate performance and limitations of inference systems to stakeholders.

Skills

Python

Deep learning

Computer vision

Model deployment

Communication

System thinking

Education

Bachelor's or Master's in CS/EE/ML

Tools

AWS

Docker

Git

SageMaker

MLflow

Jetson

Job description

We are seeking a Machine Learning Engineer to own the productionisation of computer vision models — taking trained models from research to reliable, optimised systems serving in production across both cloud infrastructure and on-device edge hardware. The role sits between model science and software engineering, requiring both ML systems depth and strong engineering discipline.

Key Responsibilities

• Deploy and maintain model serving infrastructure across both cloud (AWS EC2/SageMaker) and edge (Jetson) environments
• Optimise models for their deployment target: TensorRT engine export, INT8/FP16 quantisation, and ensemble fusion for edge; scalable serving infrastructure for cloud
• Validate accuracy-speed trade-offs across environments; define and enforce latency/throughput acceptance criteria before production rollout
• Manage model versioning and coordinated rollout across a mixed fleet of cloud-served and edge-deployed devices

Inference Pipeline Engineering

• Build and maintain end-to-end inference pipelines from model input to structured output
• Ensure inference reliability, error handling, and graceful degradation in field conditions
• Define integration contracts (input/output specs, performance envelopes) for downstream software systems
• Design and maintain evaluation frameworks: per-class performance, regression testing, benchmark reproducibility
• Build and maintain active learning pipelines to surface high-value samples for annotation
• Define annotation standards and review annotation quality in collaboration with the CV team
• Work closely with CV Scientists on model handoff requirements; with Software Engineers on API integration
• Communicate inference system performance and limitations to technical and non-technical stakeholders

Required Qualifications

Education & Experience

• Bachelor's or Master's in Computer Science, Electrical Engineering, Machine Learning, or related field
• 4+ years of experience in machine learning engineering, with production deployments to your name

Technical Skills

• Python — production-quality code; not just experimentation scripts
• Strong working knowledge of at least one major deep learning framework (PyTorch preferred)
• Solid understanding of computer vision fundamentals: object detection, image classification, model evaluation metrics (mAP, precision/recall, IoU)
• Experience with model serving and inference systems — loading, scripting, and serving trained models via REST APIs or equivalent
• Familiarity with MLOps practices: model versioning, experiment tracking (MLflow or equivalent), reproducible benchmarking
• Experience working with image datasets: understanding of annotation formats, dataset splits, class imbalance, and data quality issues
• AWS (S3, EC2, SageMaker); Docker; Git — comfortable across the full development-to-deployment workflow
• Able to write clear integration documentation: API contracts, performance envelopes, known failure modes

Soft Skills

• Methodical — validates before shipping; distinguishes a noise result from a real improvement
• Communicates ML system performance and limitations clearly to non-ML stakeholders
• Takes ownership through to production — does not consider work done at model handoff

Good to Have

• Deployment to edge hardware: Jetson Orin or comparable resource-constrained device
• Active learning pipeline design and annotation tooling (Label Studio or equivalent)
• Familiarity with multi-label or ensemble model architectures
• Experience with object tracking algorithms (ByteTrack, Kalman filter-based approaches)