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Description
As a Machine Learning Engineer, you will: - Design, build, and maintain large-scale data processing workflows, ensuring efficiency, scalability, and reliability across diverse data sources and modalities. - Develop and optimize computer vision models that power core product experiences, including areas such as image understanding, multi-view geometry, 3D reconstruction, and visual recognition. - Partner closely with engineering, research, and data teams to translate product requirements into technical solutions. This includes prototyping models, running large-scale experiments, improving data quality, and ensuring seamless integration of algorithms into production systems. - Explore emerging areas such as LLM-based agents, retrieval-augmented systems, and tool-oriented reasoning to improve internal workflows or data operations.
Minimum Qualifications
Strong foundation in computer vision, including experience with deep learning–based vision models and at least one area such as detection, segmentation, 3D vision, geometric methods, tracking, or self-supervised learning. Hands-on experience developing machine learning models using frameworks such as PyTorch or TensorFlow. Experience building or optimizing large-scale data pipelines (e.g., distributed ETL, dataset generation, annotation workflows, data validation, or high-throughput processing). Proficiency in Python or C++ for algorithm development and data processing. Experience working with distributed computing frameworks (e.g., Spark, Ray, or equivalent).
Preferred Qualifications
PhD in a relevant field with research directly related to computer vision, large-scale data systems, or multimodal learning. Experience designing or evaluating agentic systems, including LLM-powered tools, RAG pipelines, or automated data reasoning workflows. Familiarity with prompt engineering, tool-use patterns, and LLM model behavior. Experience deploying ML models at scale, including monitoring, evaluation, and continuous improvement. Knowledge of data quality assessment, dataset curation methodologies, and evaluation frameworks. Experience with GPU-based optimization, large-batch training, or distributed training. Strong multi-functional collaboration skills and the ability to lead technical initiatives.
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