Machine Learning Engineer

About

We are seeking a Machine Learning Engineer with strong expertise in computer vision and large-scale data processing. In this role, you will contribute to the development of next-generation real-time sensing and data intelligence systems by designing algorithms, building scalable data pipelines, and collaborating with multi-functional teams to deliver high-impact, production-quality solutions.

Responsibilities

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.

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.