Machine Learning Engineer

Cloud Employee, is a UK-owned Philippines business established 8 years ago. We connect high-performing software engineer talent in the Philippines with some of the world’s leading and most innovative tech companies. Developers join to work from the Philippines as part of international engineering teams and grow their CV and skill-set. ‍

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We pride ourselves on being a supportive, cutting-edge workplace that continuously invests in staff development, engagement, and well-being. We provide security, career paths, individual training programs, and mentoring.

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We are seeking a highly motivated Machine Learning Engineer to join our growing team. This role is ideal for individuals with a strong foundation in machine learning who have experience applying advanced models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs), in real-world production environments. A strong background in computer vision—particularly in object detection and tracking—is required, along with experience developing and deploying multimodal AI systems. This includes integrating LLMs and VLMs for industrial and operational use cases. You’ll work at the intersection of vision, language, and deep learning to build intelligent systems that go beyond traditional CV pipelines, enabling enhanced decision-making, automation, and insight extraction at scale.

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Our client is an innovative product and technology company driving transformation in the food industry through automation and robotics. They partner with leading restaurant and food retail brands to develop scalable, proprietary solutions that enhance kitchen operations. With a mission centered on sustainability and efficiency, their engineering team, comprising talent from top-tier hardware companies and research labs, focuses on solving real-world challenges in motion, mechanics, and intelligent automation. Among their breakthroughs is a robotic system designed to streamline high-volume kitchen prep work, already deployed at scale across major U.S. food brands.

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• Design, develop, and deploy computer vision models focused on object detection and tracking.
• Preprocess and curate large-scale image and video datasets for model training and evaluation.
• Train and fine-tune deep learning models using modern frameworks such as PyTorch and TensorFlow.
• Evaluate model performance using appropriate computer vision metrics and iterate based on results.
• Collaborate with software engineers and product managers to integrate ML models into production systems.
• Contribute to end-to-end machine learning pipelines including data ingestion, preprocessing, inference, and feedback loops.
• Stay current with academic and industry research in object detection and tracking, applying new techniques to real-world problems.
• Write clean, modular, and well-documented code using modern engineering best practices.

First 3 Months:

• Contribute to a high-priority computer vision initiative for a major U.S. Quick Service Restaurant (QSR) brand.
• Engage with curated, real-world datasets provided by the client.
• Responsibilities during this phase include:
  • Understanding the existing machine learning tech stack and models.
  • Improving and optimizing current ML data pipelines.
  • Supporting the development of a scalable data flywheel for streamlined model generation and deployment.

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• 3+ years of hands-on experience in machine learning and computer vision roles.
• Strong proficiency in Python and use of ML/CV frameworks such as PyTorch, TensorFlow, OpenCV.
• Practical experience with object detection models (e.g., YOLO, Faster R-CNN, SSD) and tracking algorithms (e.g., Deep SORT, ByteTrack).
• Solid foundation in deep learning concepts (CNNs, loss functions, optimization).
• Familiarity with cloud-based ML tools (e.g., AWS SageMaker, Azure ML, GCP).
• Experience with image/video processing libraries and annotation tools.
• Proficient in Git and collaborative development workflows.

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• Experience deploying ML models on edge devices or in constrained environments.
• Understanding of model compression techniques such as pruning or quantization.
• Exposure to real-time inference systems and performance tuning.
• Familiarity with MLOps tools and best practices (e.g., MLflow, DVC, CI/CD pipelines).

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• Competitive Salary.
• Benefits package include HMO, training allowance, gym or food allowance, educational assistance for dependents, and more.
• An annual salary increase, as laid out in the contract.
• Free food and drinks are provided during virtual events.
• Company trips.

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