Machine Learning Engineering Manager (Responsible AI & AI Safety)

Quick Facts

• Role: Machine Learning Engineering Manager (Responsible AI & AI Safety)

Description

Lead a red-teaming and Responsible AI program to proactively probe generative AI features for safety and security risks before release. Build, mentor, and develop a high-performing engineering team that defines safety policies, directs dataset and safety-alignment strategies, and oversees end-to-end mitigation architectures in production. Stay hands-on pre- and post-launch by analyzing evaluations, running failure investigations, and using human and automated feedback to continuously improve feature safety and user trust.

Responsibilities

• Lead and manage a dedicated red-teaming team and an independent safety/security probing program

• Partner with leadership, design, engineering, legal, and regulatory teams to define robust safety policies aligned with product goals

• Provide technical direction for dataset design and safety alignment strategies

• Oversee implementation of end-to-end mitigation architectures in production

• Conduct pre- and post-launch safety evaluation, failure investigations, and iterative improvement using human and automated feedback

• Communicate safety recommendations to senior leadership and remove technical roadblocks

• Foster an inclusive culture of technical excellence

Requirements

• 5+ years of machine learning experience, including generative models (Transformers, LLMs, VLMs), NLP, or Computer Vision

• 2+ years directly managing engineering teams

• Proven ability to lead technical teams through ambiguity to deliver high-quality results

• Proficiency in Python and data science libraries (e.g., Pandas) for data analysis and applied ML workflows

• Strong skills in data analysis and visualization

• Excellent communication skills for translating complex technical insights to cross-functional teams and executives

• Strong analytical and independent problem-solving skills

• Ability to interpret outputs from human and automated evaluations, especially complex multi-labeled data

• Experience collecting and analyzing language, vision, or multimodal datasets

• Background in failure analysis, quality engineering, or robustness testing for ML-driven systems

• Comfortable leading work involving sensitive or potentially offensive content

Benefits

• Opportunity to drive Responsible AI efforts for global generative AI products and build an independent red-teaming program that protects users before launch.