We work on the bleeding edge and leverage the most powerful pretrained models and algorithms, making it critical that we ensure our AI systems behave safely and align with organisational values.

You will be responsible for designing novel safety evaluation frameworks, curating high-quality data for robust evaluations and training, prototyping new safety capabilities, and developing safety-focused fine-tuning algorithms.

We're looking for outstanding individuals with deep expertise in multimodal AI safety who can translate research insights into practical solutions while being a strong communicator and collaborative teammate.

The ideal candidate takes the initiative in exploring new safety methodologies and enjoys building world-class, trustworthy AI experiences in a fast-paced applied research environment.

Microsoft's mission is to empower every person and every organisation on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realise our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

Leverage expertise in multimodal safety to uncover potential risks and develop novel mitigation strategies, including alignment techniques and robustness improvements for multimodal large language models.

Create and implement comprehensive evaluation frameworks and red-teaming methodologies to assess model safety across diverse scenarios, edge cases, and potential failure modes.

Build automated safety testing systems, generalise safety solutions into repeatable frameworks, and write efficient code for safety pipelines and intervention systems.

Maintain a user-oriented perspective by understanding safety needs from user perspectives, validating safety approaches through user research, and serving as a trusted advisor on multimodal safety matters.

Track advances in multimodal safety research, identify relevant state-of-the-art techniques, and adapt safety algorithms to drive innovation in production systems serving millions of users.

Embody our culture and values.

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Model Policy Manager, Youth Well-being

Location

San Francisco

Employment Type

Full time

Department

Safety Systems

Compensation

• Estimated Base Salary $207K – $295K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.

The Model Policy team aligns model behavior with desired human values and norms. We co-design policy _with_ models and _for_ models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. Key focus areas include: catastrophic risk, mental health, teen safety and multimodal safety.

About the Role

Providing access to powerful AI models introduces a host of challenging questions when it comes to model safety: How do we define safe behavior for how a model should behave? To what end? How do we do this in such a way that is actionable, objective and sustains replicability?

This is a senior role in which you’ll help shape policy creation and development at OpenAI and make an impact by helping ensure that our groundbreaking technologies do not create harm. The ideal candidate can identify and develop cohesive and thoughtful taxonomies of harm on high risk topics with a sense of urgency. They can balance internal and external input in making complex decisions, carefully think through trade-offs, and write principled, enforceable policies based on our values. Importantly, this role is embedded in our research teams and directly informs model training.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you’ll:

• Design model policies that govern safe model behavior in an objective and defensible way - e.g. how should the model respond in risky/unsafe scenarios? What does unsafe mean? How do we achieve safety while preserving beneficial model capabilities?

• You will develop taxonomies that inform data collection campaigns, model behaviour and monitoring strategies and also toe the line between maximizing utility and preventing catastrophic risk.

• Lead prioritization for safety efforts across the company for new model launches, understanding and addressing technical and business trade-offs.

• Develop a broad range of subject matter expertise while maintaining agility across topics.

• You will work across many internal teams which will require high organizational acumen and confident decision making.

You might thrive in this role if you:

• Have extensive experience researching LLMs, ML, AI, tech policy, moral reasoning, and/or enjoy classification problems.

• Have extensive experience defining, refining and enforcing policies for ML models across training, evaluation, and deployment.

• Understand the practical challenges of translating policy into model behavior across the full training stack, and can incorporate these constraints into policy design.

• Can reason about the benefits and risks of open-ended problem spaces, generate novel approaches under ambiguity, and take full ownership of end-to-end solutions from concept through execution.

Most relevant publications:

• Introducing HealthBench

• Preparing for future AI capabilities in biology

• Safety evaluations hub

• OpenAI GPT5 System Card

• Evaluating Fairness in ChatGPT

• Improving Model Safety Behavior with Rule-Based Rewards

• OpenAI Model Spec

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences of individuals from all walks of life.

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Microsoft are looking for a talented Member of Technical Staff - Multimodal Safety - MAI Super Intelligence Team at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As a Member of Technical Staff, Multimodal Safety, you will work to develop and implement cutting-edge safety methodologies for post-training multimodal large language models to be served to millions of users through Copilot every day. We work on the bleeding edge and leverage the most powerful pretrained models and algorithms, making it critical that we ensure our AI systems behave safely and align with organizational values.

Accountabilities

• Leverage expertise in multimodal safety to uncover potential risks and develop novel mitigation strategies, including alignment techniques and robustness improvements for multimodal large language models.
• Create and implement comprehensive evaluation frameworks and red-teaming methodologies to assess model safety across diverse scenarios, edge cases, and potential failure modes.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Proven expertise in multimodal LLM safety with experience in diffusion models and generative image/video/audio.

Personal attributes:

• Track record building evaluation frameworks, automated red-teaming, and reusable guardrail systems for safety at scale.

Benefits

• Software Engineering IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year.
• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

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