Multimodal Safety

About Microsoft AI

Microsoft AI is building AI systems and products that empower people’s lives. Our work is driven by a community of brilliant, interdisciplinary minds working across frontier model development, product engineering, and responsible AI. Within Microsoft AI, the Safety team develops the training methods, evaluations, runtime safeguards, monitoring, and infrastructure needed to make advanced AI systems safer, more reliable, and more useful. Our work spans text, multimodal, and agentic systems and is developed in close partnership with other Research teams, Production Inference, Security, Responsible AI, Microsoft product organizations, and external partners and customers.

About the Role

We are looking for a Member of Technical Staff to advance safety for generative-media models and products, with a focus on image generation and image editing. You will develop model-based classifiers, runtime guardrails, evaluations, and data that help these systems produce creative and useful content while respecting safety policy. You will work across research and production boundaries: identifying emerging failure modes from evaluations and real-world use, reproducing and diagnosing them, improving guardrails and policies, and partnering with product and platform teams to deploy and monitor safeguards at scale.

This role is a strong fit for an ML or software engineer who combines an interest in generative image systems with rigorous experimental judgment and a practical understanding of safety policy, creative quality, overblocking, latency, and real-world deployment.

Responsibilities

• Develop and improve learned classifiers, policy models, prompt-based safeguards, and other runtime guardrails for MAI's multimodal models.
• Build high-quality training and evaluation datasets from synthetic generation, red teaming, product feedback, and real-world traffic while maintaining appropriate controls.
• Design evaluations for harmful or deceptive generated media, unsafe image transformations, policy evasion, adversarial inputs, and emerging image and audio product use cases across both request and output paths.
• Turn incidents and partner feedback into repeatable test cases and regression suites.
• Diagnose failures, then translate findings into durable model, policy, or product improvements.
• Monitor deployed safeguards across traffic sources, customers, models, and product configurations.
• Partner with Production Safety and serving teams on Azure-based deployments, capacity planning, and guardrails integration.
• Partner with Safety Alignment, Safety Engine, model, product, policy, privacy, and Responsible AI teams to adjudicate ambiguous cases and define end-to-end safety strategies.
• Contribute reusable guardrail libraries, documentation, deployment and monitoring runbooks, technical standards, and operational practices for generated-media safety.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• Experience developing, adapting, or evaluating modern machine learning systems.
• Strong programming skills in Python.
• Ability to design experiments, define metrics, run ablations, and make evidence-based technical decisions.
• Experience building data pipelines, including dataset construction and annotation.
• Experience developing reproducible ML workflows using version control, configuration management, automated testing, and experiment tracking.
• Ability to collaborate across research, engineering, product, and policy disciplines and communicate technical tradeoffs clearly.

Preferred Qualifications

• Experience working with diffusion models, autoregressive media models, audio-generation systems, or other architectures used for image or audio generation and editing.
• Experience building image or audio safety classifiers, content-understanding systems, trust and safety models, policy engines, or input and output guardrails for user-facing products.
• Familiarity with generated-media safety risks such as adversarial inputs, unsafe generation or editing, harmful-content transformation, impersonation or deceptive media, privacy violations, and policy evasion.
• Experience with Azure or another major cloud platform, including Azure AI services or Azure Machine Learning, model APIs, GPU capacity planning, versioning, monitoring, and rollback.
• Experience with red teaming, adversarial evaluation, interpretability, robustness, privacy, security, policy development, or responsible AI.

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.