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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML / AI Engineer - **Company:** Gradient Corporation - **Location:** Renton, WA, United States - **Salary:** $120,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Vision, Computer Programming, Data Integrity, Data Mining, Information Extraction, Python (Programming Language), Search Algorithms, OpenCV, Pattern Recognition, Tensorflow, Data Processing, Pytorch, Large Language Models, Deep Learning, Build Management, Kubernetes, Information Technology, Feature Extraction, Software Library, Docker - **Published:** June 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=197ca293349620f6 ## About the Role Do you have experience in Machine learning libraries?, We are looking for an AI Engineer to join our team and help develop next-generation Computer Vision and Language Model systems for real-world applications. The ideal candidate will have hands-on experience with image segmentation, vector similarity search, and LLM-based feature extraction. You'll work on training and optimizing deep learning models that detect fine-grained visual or textual patterns and contribute to building scalable AI infrastructure. This is an excellent opportunity for an individual passionate about practical AI and eager to collaborate in a fast-paced start-up environment alongside experienced engineers and researchers., * Currently pursuing or holding a degree in Computer Science, Electrical Engineering, Artificial Intelligence, or related fields. * Strong programming skills in Python with experience using PyTorch, TensorFlow, or JAX. * Familiarity with Computer Vision tasks such as segmentation, detection, and classification using frameworks like OpenCV, MMDetection, or Detectron2. * Understanding of embedding spaces, vector databases, and similarity search techniques (FAISS, Annoy, Milvus, Pinecone, etc.). * Experience fine-tuning or prompting Large Language Models (LLMs) for structured information extraction or classification tasks. * Exposure to multimodal models (e.g., CLIP, BLIP, Florence, or Segment Anything Model). * Proficiency in data wrangling and labeling pipelines, including dataset curation and annotation tools. * Docker and container orchestration * Strong analytical and problem-solving abilities, with attention to data quality and reproducibility. * Excellent collaboration and communication skills across multidisciplinary teams. * A self-starter mindset with curiosity for exploring emerging AI research and applying it in production environments. ## Description * Develop and train segmentation and defect detection models for identifying visual features such as surface wear, shape anomalies, or alignment inconsistencies. * Build and optimize image embedding and vector similarity search pipelines (e.g., using FAISS, Milvus, or Pinecone) for matching against large reference datasets. * Experiment with pattern recognition and feature consistency methods to identify relationships between visually similar items or product variants. * Apply LLM-based feature extraction to interpret structured and unstructured text-automatically deriving attributes like names, identifiers, years, and categorical fields from images or metadata. * Design and build end-to-end data extraction pipelines that parse unstructured sources into clean, validated, production-ready datasets. This includes schema design, multi-pass parsing with edge case handling, cross-column validation, and iterative QA cycles to ensure data integrity at scale. * Evaluate and fine-tune model performance across multiple architectures and modalities (vision, text, or multimodal). * Collaborate closely with software and data engineers to integrate AI models into production pipelines and APIs. * Research and prototype state-of-the-art AI techniques in segmentation, embedding learning, and multimodal understanding. * Maintain thorough documentation of experiments, models, and deployment results. ## Related Videos - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Deepfakes in Realtime - How Neural Networks Are Changing Our World](https://www.wearedevelopers.com/videos/180-deepfakes-in-realtime-how-neural-networks-are-changing-our-world) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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