[{"Salary":null , "Remote_Job":false , "Posting_Title":"Staff ML Engineer (Computer Vision)" , "Is_Locked":false , "City":"Bangalore North","Industry":"IT Products","Job_Description":" About the Role: Maximl\u2019s ML Engineering Team is looking to hire a Staff ML Engineer (Computer Vision) that can take on a technical leadership role within the team. Key Responsibilities Architect and design end-to-end CV systems Lead the definition of system-level requirements, data flows, model architectures, and deployment pipelines to ensure scalable, maintainable, and high-performance computer-vision solutions. Drive research & innovation Stay abreast of the latest academic and industry advances in computer vision, like - object detection, segmentation, visual-language models, reasoning, OCR, video processing etc.\u2014evaluate and integrate promising new techniques. Prototype and validate new models Rapidly develop proof-of-concepts, run thorough A/B experiments, and establish robust evaluation benchmarks to de-risk technology choices before production. Optimize for production at scale Lead efforts to compress, quantize, and accelerate models for cloud GPUs/TPUs and edge devices; integrate with CI/CD, automated testing, and containerized MLOps frameworks. Mentor and coach engineers Provide technical leadership through code reviews, design reviews, brown-bag talks, and best-practice guidelines; help mid- and junior-level engineers grow their CV expertise and career. Collaborate cross-functionally Work closely with product engineering teams, solution delivery, customer success and end customers to translate business requirements into technical specs, ensure data quality and availability, and deliver features that solve real customer safety-compliance problems. Define and enforce coding standards Establish style guides, testing protocols, performance budgets, and documentation standards for CV projects; champion reproducibility and clean, well-documented code. Lead technical architecture decisions Evaluate and select frameworks, libraries, annotation tools, and compute infrastructure; balance trade-offs between flexibility, performance, and cost. Monitor and improve model performance Set up monitoring dashboards and alerting for model drift, accuracy regressions, and runtime failures; spearhead root-cause analyses and continuous retraining strategies. Requirements 6-8 years prior experience building, deploying and managing production-scale Computer Vision (CV) systems using modern deep learning models and MLOps tools and deployed on cloud infrastructure (AWS/GCP/Azure). Has trained and deployed in production, computer vision models for use-cases like object detection, visual question answering, image classification, visual attribute extraction, text detection & extraction / OCR etc. Practical experience working on model optimization for high-performance/low-cost inference for cloud GPUs. Prior experience with multiple of the following: YOLO, TensorFlow, PyTorch, NVidia TensorRT, Triton Server etc. Practical experience leading team of ML Engineers / Data Scientists Experience optimizing models for mobile/edge devices. Benefits Flexible Working Hours Health Insurance Accidental Insurance Subsidised Food Options Unlimited Beverages ","Work_Experience":"6+ years","Job_Type":"Full time","Job_Opening_Name":"Staff ML Engineer (Computer Vision)" , "State":"Karnataka" , "Country":"India" , "Zip_Code":"560038" , "id":"65263000005424060" , "Publish":true , "Date_Opened":"2025-05-29" , "Keep_on_Career_Site":false}]

Model Development Implement and fine-tune moderndetection/classification architectures like YOLOv8, Faster/Mask RCNN,EfficientNet, ConvNeXt, and Vision Transformers. Conduct ablation studies,hyperparameter tuning, and model optimization experiments (e.g., pruning,quantization, distillation). Data Pipeline Define labeling guidelines,manage annotation QA loops, and handle class imbalance strategies likere-sampling or focal loss. Build data loaders andaugmentation pipelines using libraries such as Albumentations or TorchVision,tailored to challenging industrial imagery. Evaluation & QA Design reproducible experimentswith clear metric dashboards (mAP, F1 score, PR curves). Perform error analysis andmodel debugging to uncover edge-case failure modes. Deployment Package models for deploymenton cloud services (e.g., Azure, AWS). Integrate models intoproduction workflows using REST APIs, Docker, and CI/CD pipelines. Collaboration Work closely withcross-functional teams to translate real-world use cases into model specs. Document code and experimentsthoroughly and contribute to weekly research reviews. Requirements Key Requirements for the Role : 13 years hands-on experience in computer vision ordeep learning roles Experience with industrial/safetyinspection datasets (e.g., PPE detection, visual defect classification). Familiarity with MLOps tools likeMLflow, DVC, or ClearML. Experience with model optimizationand deployment frameworks (ONNX, TensorRT, OpenVINO). Exposure to real-time or edgeinference performance constraints. Contributions to open-source,research publications, or competitive CV challenges (e.g., Kaggle). Technical Qualification: Proficiency in Python and deep learning frameworks (PyTorch /Tensorflow). Good understanding of CNNs, transfer learning, data augmentation, and overfitting mitigation. Familiarity with basic software engineering practices (git, code reviews, unit testing). Mathematical Background: Solid grasp of linear algebra, probability, and optimization as applied in ML . Languages/Frameworks: Python, PyTorch, Tensorflow, TorchVision, FastAPI, OpenCV Model Tools: ONNX, TensorRT, Albumentations DevOps : Docker, Git, Azure/AWS Infra: Jetson devices, cloud APIs, SQL/NoSQL databases