Associate Machine Learning Engineer

📋 Job Overview

Application\nJob Title: Associate Machine Learning Engineer\nDepartment: Engineering / AI\nLocation: On-Site, H-12, NSTP, Islamabad

🏢 About ImagineArt

We're redefining how the world creates and designs.\nImagineArt is one of the fastest-growing GenAI companies in the world. We've scaled faster than most funded startups, with zero outside funding.\n100M+ social impressions\nBuilt and shipped our own image generation model, now ranked #3 globally for photorealism\nNo funding. No shortcuts. Just a sharp, driven team building one of the strongest GenAI products in the world, and we're just getting started.\nIf you want to build something that truly matters, work with exceptionally smart people, and grow at a pace most companies never experience, ImagineArt is the place.

🎯 The Role

We are looking for an Associate Machine Learning Engineer who is passionate about building intelligent systems and working with modern machine learning technologies.\nYou will work alongside experienced ML engineers and researchers to develop, experiment with, optimize, and deploy machine learning models powering our AI products.\nThis role is ideal for someone with a strong foundation in machine learning, good programming skills, and a genuine interest in Generative AI, deep learning, and large-scale AI systems.\nYou won't just be working with existing models or running notebooks. You'll get hands-on experience building actual ML pipelines, experimenting with model architectures, improving inference performance, and taking ML solutions from experimentation to production.

✅ Key Responsibilities

• Machine Learning & Model Development\nDevelop, train, evaluate, and improve machine learning models for real-world applications.\nWork with deep learning architectures and modern ML frameworks.\nAssist in implementing and experimenting with neural networks, transformers, and generative models.\nPerform data preprocessing, feature engineering, model evaluation, and error analysis.\nExperiment with different model architectures, training configurations, and hyperparameters.
• Generative AI & Deep Learning\nExplore and work with modern Generative AI technologies, including diffusion models, transformers, and LLMs.\nUnderstand and experiment with architectures behind image, video, and other generative AI systems.\nWork with pretrained models and adapt them for specific product requirements.\nExplore techniques such as fine-tuning, transfer learning, LoRA, and other model optimization approaches.\nStay updated with recent ML research and identify techniques that can improve our products.
• ML Engineering & Deployment\nBuild and maintain ML training and inference pipelines.\nWork with PyTorch and other machine learning frameworks to implement and optimize models.\nAssist in deploying models into production environments.\nOptimize inference latency, GPU utilization, and memory consumption.\nCollaborate with backend engineers to integrate ML models into scalable production systems.\nWrite clean, modular, and maintainable Python code.
• Research & Experimentation\nRead and understand relevant research papers and translate useful ideas into working implementations.\nRun experiments, analyze results, and document findings.\nBenchmark models against performance, quality, and efficiency metrics.\nIdentify opportunities to improve existing models and ML workflows.

📌 Required Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Must Have
• 0–2 years of experience in Machine Learning, Deep Learning, or relevant practical projects. Must Have
• Strong programming skills in Python. Must Have
• Good understanding of fundamental Machine Learning concepts, including supervised and unsupervised learning, model evaluation, and optimization. Must Have
• Hands-on experience with PyTorch or TensorFlow. Must Have
• Understanding of neural networks, backpropagation, gradient descent, and common deep learning architectures. Must Have
• Familiarity with NumPy, Pandas, and Scikit-learn. Must Have
• Understanding of CNNs, Transformers, or other deep learning architectures. Must Have
• Experience implementing ML projects beyond basic tutorials or coursework. Must Have
• Familiarity with Git, Linux, and basic software development practices. Good to Have

⭐ Desirable Experience

• Exposure to Generative AI, LLMs, diffusion models, or computer vision. Good to Have
• Familiarity with Hugging Face, pretrained models, and model fine-tuning. Good to Have
• Understanding of CUDA, GPU computing, or model inference optimization. Good to Have
• Experience reading and implementing research papers. Good to Have
• Strong problem-solving skills and willingness to experiment, learn, and build.

🎁 Benefits

Why Join ImagineArt\nWork on innovative AI products used by creators around the world.\nGet hands-on exposure to real-world Generative AI systems and production ML infrastructure.\nLearn directly from experienced engineers working on challenging AI problems.\nWork with modern GPUs, ML frameworks, and cutting-edge AI technologies.\nTake ownership of meaningful projects and see your work impact millions of users.\nGrow in an environment that encourages experimentation, research, and engineering excellence.

🛂 Visa & Eligibility