> Markdown version of [/jobs/ext/239747-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/239747-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** TERRACLEAR, INC. - **Location:** Issaquah, WA, United States - **Experience:** Experienced - **Salary:** $150,000.0 - $220,000.0 - **Contract:** Internship / Graduate position - **Skills:** Clean Code Principles, Amazon Web Services, Computer Vision, Cloud Computing, Configuration Management, Distributed Computing Environment, Machine Learning, Smart Devices, Graphics Processing Unit (GPU), Pytorch, Containerization, ONNX (Open Neural Network Exchange) Format, Production Code, Slurm, Machine Learning Operations, TensorRT, Software Version Control, Docker - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=71f2a08cbeb38858 ## About the Role Required 3+ years of full-time professional experience in computer vision-focused machine learning (not student internship) Strong PyTorch experience, including custom layers, loss functions, datasets, dataloaders, and training loops Experience with modern vision architectures (CNNs, ViTs, DETR-style models, foundation models) Experience building or contributing to production ML systems Solid software engineering fundamentals (testing, version control, clean code principles) Strong communication skills and ability to explain complex technical topics clearly * Engineering degree or equivalent practical experience Preferred Experience deploying models to edge devices (Jetson, embedded GPUs, mobile platforms) Experience with AWS or similar cloud infrastructure Experience with Docker and containerized ML workflows Familiarity with robotics or perception systems * Experience owning or contributing to a production model that delivered business value ## Description As a Machine Learning Engineer, you will design, train, evaluate, and deploy deep learning models for real-world computer vision applications across mapping intelligence and robotics systems. This is a hands-on engineering role. You will write production code, build scalable training pipelines, and improve ML infrastructure. You'll collaborate closely with other ML engineers, software engineers, and product stakeholders to continuously improve model performance in real-world environments. Our systems operate in challenging environments where robustness, generalization, and performance matters. The models you build will directly impact field operations and autonomous decision-making. In this role you will: Design and train modern computer vision models (CNNs, Vision Transformers, foundation models) to solve novel perception tasks Build and maintain scalable training pipelines using PyTorch and HPC infrastructure (e.g., Slurm, distributed training) Develop data curation and active learning workflows Optimize models for deployment (ONNX, TensorRT, containerization) Test and validate models in both cloud and edge environments Build reproducible experimentation workflows (version control, experiment tracking, configuration management) Drive experimental cycles: define hypotheses, implement techniques from literature, evaluate results, and present recommendations Translate research ideas into production-ready implementations Design, implement, and test ML-related components and supporting software * Perform statistical analysis and fine-tune model performance based on production and field feedback ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML ](https://www.wearedevelopers.com/videos/78-leverage-cloud-computing-benefits-with-serverless-multi-cloud-ml) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)