Machine Learning Engineer

About Us

KDCI Outsourcing is a trusted partner for global businesses, providing world-class talent solutions across creative, technology, marketing, finance, and back-office functions. We pride ourselves on helping innovative companies scale with specialized teams in emerging technologies.

As part of our technology practice, we are seeking a Machine Learning Engineer who will play a critical role in developing advanced AI and computer vision solutions for real-world applications in robotics and automation.

Role Overview

The Machine Learning Engineer will be responsible for building and improving the tools, infrastructure, and pipelines that power AI model training. This role will focus on enhancing synthetic data pipelines, automating training evaluations, and developing systems that connect data quality to model performance.

You will collaborate with cross-functional experts in machine learning, simulation, and software engineering to deliver scalable, production-grade systems designed for robust AI deployment in industrial environments.

Key Responsibilities

• Identify opportunities to improve synthetic data pipelines and deliver enhancements that increase dataset quality, scalability, and control.

• Propose and implement new data-generation or augmentation strategies to address training bottlenecks and improve model generalization.

• Collaborate with ML scientists, engineers, and technical specialists to transform concepts into robust, production-grade solutions.

• Design and implement tooling to configure and scale synthetic data generation.

• Run structured training experiments to measure the impact of new approaches and benchmark results.

• Build internal tools to expose dataset properties, monitor changes, and support data-driven improvements.

Required:

• Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or related technical field, or equivalent experience.

• Strong proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.

• Solid understanding of ML workflows, including dataset preparation, model evaluation, and performance diagnostics.

• Experience with synthetic data generation, simulation environments, or 3D rendering tools (e.g., Blender, Unity).

• Ability to write and maintain production-quality code in Linux/Docker environments.

Preferred:

• Background in computer vision (object detection, segmentation, 6-DoF pose estimation).

• Knowledge of sim-to-real transfer, domain randomization, or robotics applications.

• Familiarity with cloud-based ML infrastructure.

• Ability to design, execute, and interpret structured training experiments.