Applied ML Engineer

Key Responsibilities:

• **Own the Feedback Loop:** Monitor production data to pinpoint areas where models struggle within specific customer environments. - **Diagnose & Propose:** Analyze discrepancies between model output and real-world data to suggest actionable algorithmic or data-driven solutions. - **Continuous Validation:** Take charge of the "last-mile delivery" by setting up appropriate use-cases and validating the accuracy of final results for customers. - **Drive Data-Centric Improvements:** Lead the data "flywheel" by curating specialized datasets and integrating valuable customer data for model retraining. - **Operationalize SoTA:** Transform advanced architectures into efficient, cost-effective solutions tailored to individual customer use-cases. - **Validate for Impact:** Develop evaluation frameworks that assess true customer value beyond standard benchmarks. Qualifications Required:

• Bachelor's / Master's degree in Computer Science (or related field) or Civil Engineering with a focus on Data Science / Lean Construction. - Proficiency in training and evaluating Deep Learning models using PyTorch. - Strong grasp of fundamental concepts in machine learning and computer vision, particularly in representation learning. - Experience in training and fine-tuning Deep Learning architectures for Object Detection, Segmentation, and Large Vision Language Models. - Familiarity with data curation, visualization, outlier detection, active learning, and hard-negative mining. - Proficient in Python with solid software engineering skills. - Ability to collaborate effectively in a team environment. - Excellent problem-solving abilities. - Strong communication and interpersonal skills with fluency in English. Key Responsibilities: