Junior ML Engineer (Computer Vision)

Job Summary


Key Responsibilities

• 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.; evaluate 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

• 1-2 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
• Experience optimizing models for mobile/edge devices.

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