AI/ML Engineer – Search

Job not on LinkedIn

October 3

Apply Now

Receive Emails with Similar Jobs

Fractional Jobs

WebsiteLinkedInAll Job Openings

eCommerce • Productivity • AI

Fractional Jobs is an innovative platform that leverages AI technology to streamline the image editing process for businesses. With tools like background removal, automatic resizing, and intelligent cropping, Fractional Jobs allows users to efficiently manage and enhance large volumes of images for eCommerce and marketing purposes. Their services are designed for wholesalers, marketplace sellers, and other businesses needing quick and high-quality image edits, thereby reducing turnaround times and improving overall productivity.

0 - 1 employees

Founded 2023

🛍️ eCommerce

⚡ Productivity

📋 Description

• Build and extend backend services that power AI-driven media search and metadata enrichment • Develop, integrate, and deploy AI/ML inference pipelines (embeddings, vision/audio models, transcription, background removal, etc.) • Fine-tune and optimize computer vision and generative models (e.g., U²Net, BiRefNet, CLIP, Whisper, YOLO, diffusion models) • Work with large datasets (100k–5M images): preprocessing, augmenting, and structuring for training/inference • Contribute to building pipelines for tasks like background removal, inpainting/outpainting, banner generation, logo/face detection, and multimodal embeddings • Integrate with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) for similarity and semantic search • Collaborate with the engineering team to deploy scalable AI inference endpoints (Docker + GPU/EC2/SageMaker)

🎯 Requirements

• 2–3 Years • Core Python (Required) – solid programming and debugging skills in production systems • AI/ML Libraries – hands-on experience with PyTorch and/or TensorFlow, NumPy, OpenCV, Hugging Face Transformers • Model Training/Fine-Tuning – experience fine-tuning pre-trained models for vision, audio, or multimodal tasks • Data Handling – preprocessing and augmenting image/video datasets for training and evaluation • Vector Search – familiarity with FAISS, Pinecone, or similar for embeddings-based search • Comfortable with chaining or orchestrating multimodal inference workflows (e.g., image + audio + OCR → unified embedding