Machine Learning Evaluation Engineer

We are looking for a highly motivated Machine Learning Evaluation Engineer to join our team and help define and drive the evaluation of advanced machine learning and computer vision technologies. You will work closely with algorithm data and engineering teams to build scalable evaluation methodologies uncover model weaknesses and turn complex data into actionable will play a key role in ensuring that our ML systems deliver high-quality robust experiences across diverse real-world scenarios. The ideal candidate combines strong fundamentals in classical machine learning and computer vision with hands-on experience in metrics design data analysis failure analysis visualization and evaluation infrastructure. You should also be comfortable leveraging modern AI-assisted tools and workflows to improve engineering efficiency and accelerate analysis.

In this role you will define and drive the evaluation strategy for computer vision and machine learning algorithms used in complex product experiences. You will work closely with algorithm engineers to understand system behavior identify the most meaningful quality signals and develop evaluation frameworks that reflect real-world will design metrics and evaluation methodologies that go beyond aggregate accuracy and help the team understand performance across important data slices and scenarios. You will analyze large-scale datasets to identify gaps in data quality and coverage and develop strategies to improve the representativeness of training and evaluation significant part of this role will involve failure analysis. You will investigate model failures identify recurring patterns develop failure taxonomies and determine whether issues are driven by data labeling algorithm limitations environmental conditions or other system-level factors. You will build tools and visualizations that enable engineers to efficiently explore failures and understand the underlying root will also develop scalable and automated workflows for evaluation analysis and reporting. You will be expected to leverage modern AI-assisted tools where appropriate to accelerate data analysis visualization development coding and workflow automation while maintaining technical rigor and a member of the team you will help shape evaluation best practices influence algorithm and data decisions and drive improvements across the ML development lifecycle. You should be comfortable navigating ambiguity independently identifying opportunities for improvement and partnering with cross-functional teams to deliver high-quality ML systems.

BS and a minimum of 3 years relevant industry experiencen3 years of relevant industry experience in machine learning computer vision algorithm evaluation data science or a related background in machine learning and computer vision including classical ML and statistical modeling experience designing and implementing evaluation methodologies and quality metrics for ML expertise in failure analysis and root-cause analysis with the ability to identify systematic model weaknesses and translate findings into actionable analyzing data quality diversity representativeness and coverage gaps across large and complex programming skills in Python with experience in data processing analysis and building automated and scalable evaluation pipelines and to develop clear and insightful visualizations and dashboards that communicate model performance regressions and failure understanding of statistical analysis experimentation and performance to independently drive ambiguous technical problems from problem definition through analysis and using AI-powered development and analysis tools to improve productivity accelerate data exploration automate repetitive workflows and improve engineering efficiency.

MS in Computer Science Computer Engineering Electrical Engineering Statistics Applied Mathematics or a related technical field (Advanced degree is a plus)nExperience evaluating computer vision algorithms such as object detection classification tracking segmentation pose estimation or hand with large-scale ML datasets and data developing internal tools or platforms for ML evaluation and with deep learning frameworks and modern ML with synthetic data data augmentation or automated data with model monitoring regression detection and production ML quality applying generative AI or AI-assisted workflows to engineering and analytical mentoring engineers or providing technical leadership for complex evaluation communication skills and the ability to collaborate effectively across algorithm engineering data and product teams.