Machine Learning Engineer

Machine Learning Engineer II, Computer VisionHouston, TX | Remote | Hybrid · Full-time · $100,000 – $150,000Are you the right candidate for this opportunity Make sure to read the full description below.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.WHAT YOU'LL DOTrain, fine-tune, and evaluate object detection models (e.g., YOLO-family, DETR-family, Faster R-CNN) for production use casesBuild and maintain data pipelines for image and video datasets, including labeling workflows and quality checksDefine evaluation metrics (mAP, per-class precision/recall, IoU, latency) and run error analysis to decide what to fix nextOptimize models for deployment through quantization, pruning, and export to ONNX or TensorRTDeploy and monitor models in production, tracking accuracy drift and data distribution shiftRun experiments with clear tracking and reproducible resultsWrite clean, tested Python code and take part in code reviewsShare findings with product and engineering stakeholders in plain termsWHAT YOU'LL BRING2–5 years of professional experience building ML systems, with 2+ years focused on computer visionAt least one object detection model shipped to production and maintained after launchStrong Python skills and hands-on experience with PyTorchWorking knowledge of modern detection architectures, including one-stage, two-stage, and transformer-based detectorsSolid grasp of detection fundamentals: anchor-based and anchor-free methods, IoU, Non-Maximum Suppression, mAP, and class imbalanceExperience with image augmentation, dataset curation, and annotation quality controlComfort with Git, Linux, Docker, and training on GPUsBS in Computer Science, Computer or Electrical Engineering, or a related fieldNICE TO HAVEMS or PhD with a computer vision focusExperience with multi-object tracking, instance segmentation, or video-based detectionMLOps experience: experiment tracking, model registries, CI/CD for models, and production monitoringDistributed 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 visionOUR TECH STACKLanguage: PythonFrameworks: PyTorch, torchvision, UltralyticsImage processing: OpenCV, Albumentations, PillowInference: ONNX Runtime, TensorRT, Triton Inference ServerInfrastructure: Docker, Kubernetes, AWS/GCP GPUsTHE DETAILSLevel: Mid-level (IC2 / L4 equivalent)Team: Machine Learning, reporting to Justin SerowikLocation: Houston, TX, remote, or hybridCompensation: $100,000 – $150,000 base salaryStart date: As soon as possibleDon't meet every qualification? xhyhwjd 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. Apply today.