Machine Learning Engineer (Python / Data Science)

Machine Learning Engineer II, Computer Vision Houston, TX | Remote | Hybrid · Full-time · $100,000 – $150,000 Imagine a warehouse team, a farmer, or a city planner training a state-of-the-art object detector on their own images, without needing an ML team. That's what we're building, and we'd like you to help build it. As a Machine Learning Engineer II on our ML team, you'll design the systems that turn raw images into labeled datasets, run training at scale on GPU clusters, benchmark every model, and put it into production with a single click. Every improvement you ship gives thousands of customers a better model. You'll own real features from day one, work closely with data, platform, and product teams, and have senior engineers in your corner as you grow. Along the way, you'll gain deep insight into how computer vision models work and how to make them better. Build and maintain data pipelines for image and video datasets, including labeling workflows and quality checks Define evaluation metrics (mAP, per-class precision/recall, IoU, latency) and run error analysis to decide what to fix next Optimize models for deployment through quantization, pruning, and export to ONNX or TensorRTDeploy and monitor models in production, tracking accuracy drift and data distribution shift Write clean, tested Python code and take part in code reviews2–5 years of professional experience building ML systems, with 2+ years focused on computer vision At least one object detection model shipped to production and maintained after launch Strong Python skills and hands-on experience with PyTorchWorking knowledge of modern detection architectures, including one-stage, two-stage, and transformer-based detectors Experience with image augmentation, dataset curation, and annotation quality control Comfort with Git, Linux, Docker, and training on GPUsBS in Computer Science, Computer or Electrical Engineering, or a related fieldMS or PhD with a computer vision focus Experience with multi-object tracking, instance segmentation, or video-based detectionMLOps experience: experiment tracking, model registries, CI/CD for models, and production monitoring Distributed training across multiple GPUsEdge deployment (NVIDIA Jetson, mobile, or embedded) and inference optimization with ONNX Runtime or TensorRTPublications, open-source contributions, or competition results in computer vision

As soon as possible~ If you've shipped detection models and you're excited about putting that power in more people's hands, we'd still love to hear from you.