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Senior Machine Learning Engineer

ValueLabs, Inc.United States🇺🇸United StatesPosted Sep 16, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
22 hours ago
AWSMachine LearningComputer VisionDeep Learning

Job Description

  • Design, develop, train, evaluate, and deploy production-grade machine learning and deep learning models for computer vision applications.
  • Build end-to-end machine learning pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
  • Train deep neural networks from scratch on large-scale image datasets and optimize model architectures for accuracy, latency, scalability, and robustness.
  • Develop computer vision solutions for image classification, object detection, segmentation, localization, image similarity, and feature extraction.
  • Own the complete machine learning lifecycle, including experiment design, hyperparameter optimization, model versioning, model registry, reproducible training pipelines, and model performance monitoring.
  • Design and optimize distributed training pipelines utilizing multiple GPUs and efficiently process large-scale datasets.
  • Evaluate model performance using statistical methods, rigorous experimentation, and business-centric success metrics.
  • Apply model explainability techniques to validate, interpret, and communicate model predictions.
  • Build scalable training and inference pipelines using AWS SageMaker and other cloud-native services.
  • Collaborate closely with Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, Data Engineers, QA teams, domain experts, and business stakeholders to deliver production-ready AI solutions.
  • Drive continuous model improvements through hypothesis-driven experimentation, error analysis, performance optimization, and data-driven decision making.
  • Lead and mentor Machine Learning Engineers, Data Scientists, and Software Engineers.
  • Provide technical direction, establish engineering best practices, conduct architecture and code reviews, and drive execution of large-scale machine learning initiatives.

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