Machine Learning Research Engineer

Quick Facts

• Role: Machine Learning Research Engineer (ML Research)
• Focus: Advanced face understanding and analysis (visual identity, facial attributes, liveness/anti-spoofing, eye tracking/gaze)

Description

You will design, implement, and deploy state-of-the-art ML solutions for advanced face analysis and understanding, partnering closely with feature and product teams. You will leverage large-scale visual/multimodal foundation models, adapting or distilling them via training adapters or other efficiency approaches to meet feature-specific needs. Ensuring quality in the field—with an emphasis on fairness and model robustness—is an important part of the role.

Responsibilities

• Design, implement, and deploy state-of-the-art ML solutions for advanced face analysis and understanding
• Use pre-trained large-scale visual/multimodal foundation models via training adapters or distilling to smaller footprints as required
• Communicate results and insights to partners and leaders, providing clear and actionable recommendations
• Stay abreast of the latest trends, technologies, and best practices in computer vision, machine learning, and large-scale foundation models
• Actively contribute to the ML community by disseminating research ideas and results, enhancing shared infrastructure, and mentoring fellow practitioners

Requirements

• M.S. or Ph.D. in Computer Science or Computer Engineering (or similarly related fields), or comparable professional experience
• Extensive knowledge and experience leveraging large-scale foundation models (including multimodal LLMs or vision-language models), including adapting/distilling for downstream perception tasks
• Strong programming skills in Python and proficiency in PyTorch or equivalent deep learning frameworks
• Proficiency leveraging AI coding and productivity tools to accelerate experimentation and model training iterations

Benefits

• Opportunity to build ML technologies enabling real product experiences for face understanding and analysis
• Cross-functional collaboration with ML leaders, researchers, software engineers, and hardware/design teams to ship new experiences
• Emphasis on real-world quality, including fairness and model robustness