Machine Learning Computer Vision Engineer – End-to-End Vision Systems (2 contract years)

ST ENGINEERING IHQ PTE. LTD.; Singapore; Hybrid; SGD 90,000 - 120,000; Full time

Job summary

A leading technology firm in Singapore is seeking an experienced ML CV Engineer to develop and optimize computer vision pipelines. This role involves end-to-end management of CV models, including deployment and monitoring, within a collaborative team environment. Candidates should have substantial ML experience and proficiency with tools like PyTorch and Docker. A hybrid work setup offers flexibility while contributing to next-gen AI projects.

Benefits

Autonomy and ownership

Access to international AI researchers

Hybrid work setup

Qualifications

• 6+ years of ML or CV engineering experience.
• 3+ years building production-grade vision systems.
• Proficiency in modern CV frameworks.

Responsibilities

• Build computer vision pipelines for data ingestion and preprocessing.
• Train and optimize CV models with PyTorch and modern frameworks.
• Deploy models using Docker and expose via APIs.

Skills

ML or CV engineering

Production-grade vision systems

PyTorch

Docker

Kubernetes

Tools

MLflow

Airflow

Prometheus

Job description

Overview

We’re looking for a hands-on ML CV Engineer to lead the development and deployment of robust, production-grade computer vision pipelines. In this role, you’ll own the full lifecycle of CV models - from data curation and preprocessing, through model training and evaluation, to deployment, monitoring, and automated retraining.

You’ll play a critical role in ensuring our vision systems remain accurate, responsive, and scalable under real-world conditions. Your work will directly impact applications involving image classification, object detection, segmentation, and other visual inference tasks.

This is a role for someone who thrives in full-stack ML development, combining deep modeling expertise with disciplined engineering and deployment practices.

Key Responsibilities

• End-to-End Vision Systems
  • Build computer vision pipelines covering data ingestion, cleaning, augmentation, and preprocessing.
  • Train and optimize CV models (classification, detection, segmentation) with PyTorch, TorchVision, and modern frameworks (YOLO, Detectron2, MMDetection, DINO).
  • Automate evaluation workflows to benchmark performance and detect drift over time.
• Production Deployment & Integration
  • Deploy models with containerized environments (Docker, TorchServe, ONNX Runtime, BentoML) and expose via APIs (REST/gRPC).
  • Collaborate with engineers to integrate models into larger platforms with reliability at scale.
• Automation & Orchestration
  • Design automated pipelines for data validation, retraining, and deployment (RPA).
  • Implement workflow orchestration with Airflow, Prefect, or Dagster for scheduled training, monitoring, and failure recovery.
• Monitoring & Reliability
  • Monitor production performance, detect drift, and handle recovery gracefully.
  • Build alerting and observability with Prometheus, Grafana, or OpenTelemetry.
• Collaboration & Tooling
  • Contribute to MLOps tooling for reproducibility, experiment tracking, and data versioning (MLflow, wandb).
  • Work with AI Engineers to ensure clean integration with orchestration frameworks.

Must-Have Skills

• 6+ years of ML or CV engineering, including 3+ years building production-grade vision systems.
• Strong knowledge of CV tasks and architectures (classification, detection, segmentation).
• Proficient in PyTorch, TorchVision, Albumentations, and modern CV frameworks.
• Proven experience training and tuning models on real-world datasets.
• Skilled in production deployment (Docker, TorchServe, ONNX Runtime, BentoML, Kubernetes).
• Strong software engineering foundation: clean Python, Git workflows, testable architecture.
• Experience with ML orchestration tools (Airflow, Prefect, Dagster).
• Familiarity with monitoring and alerting systems for ML models.

What We Offer

• Small, agile team (5–6 engineers + interns) with autonomy and real ownership.
• Startup feel with a big company resources: International environment where the majority of the team and leadership is from startups or big international corporations (Lazada, Gojek, IBM) and from various countries.
• Low-bureaucracy, high-impact startup environment where your code directly supports next-gen AI deployment.
• Experimentation and self-development are in our culture
• Knowledge sharing and collaboration
• Direct collaboration with top AI researchers and computer vision scientists.
• Hybrid work setup: ~2–3 days in office per week.