Model Behavior Engineer Jobs

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a User Researcher on the Economy team, reporting directly to the Director of Design Strategy & Operations, you will generate actionable qualitative and quantitative insights that shape core economic systems, developer monetization workflows, and immersive brand experiences. Embedded directly on product teams, you will conduct generative and evaluative research centered on how creators build businesses, how brands interact on the platform, and how users participate in virtual economies. You will tackle high-priority features, solve complex behavioral puzzles, and help translate multi-faceted economic infrastructure into intuitive, trustworthy, and scalable experiences. Partnering closely with Product, Product Design, Engineering, Data Science, and Policy, you will ensure user needs directly inform platform decisions while driving mutual value across players, creators, and brands. You will: Execute end-to-end mixed-methods research projects to understand creator monetization, payouts, advertising, and commerce workflows, translating abstract user behaviors into clear product feature recommendations. Conduct foundational research to uncover key user pain points, mental models, and behav

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 frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. 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 and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. Develop structured taxonomies of chemical and biological risk that inform model training data, evaluation benchmarks, and safet

About the Team Our 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 frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. Our relevant publications: Preparedness framework 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 Your Responsibilities: Design and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. Develop structured taxonomi

About the Team The Core Models team shapes how our models interact with people. We view the model as the product itself, aiming for intuitive experiences that exceed user expectations and feel like magic. About the Role As a Model Designer, you’ll have an outsized impact on how our models interact and resonate with users. You’ll strike a delicate balance between maximizing the model’s capabilities, reading in between the lines in user queries to understand how best to help, and upholding user trust. We’re looking for people who are passionate about the intersection of design, technology, and user experience — and are up for the challenge of defining new human-AI interaction paradigms. 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 join a small team in evolving and expanding the model design function. You will: Collaborate closely with researchers to understand, predict, and design model behavior. Partner with product managers and designers across the company to ensure a cohesive voice and approach. Proactively identify ways to improve our models based on product sense, user feedback, quantitative insights, and the research roadmap. Come up with creative strategies for collecting high-quality data. Do whatever needs to be done to make our models better. You might thrive in this role if you: Possess exceptional taste, creativity, and writing skills, allowing you to craft responses that delight users. Don’t mind ambiguity — you’re happy to throw yourself into a new, unfamiliar environment, build relationships, define a problem, and make progress. Love experimentation, and are willing to test and reject new ideas when the results don’t pan out. Exhibit high levels of empathy and self-awareness required to serve everyone in the world. Enjoy tackling profound and often philosophical questions while always driving towards clarity. Demonstrate technic

About the Team OpenAI is at the center of some of the highest-impact multimodal work in AI. ChatGPT serves a massive global audience, and enables diverse interactions via text, speech, and visuals. As interactive surfaces grow, models also need to adapt to emerging harm, understand user intent and situational context, and respond appropriately. The Chat and Multimodal Safety team is responsible for ensuring that OpenAI’s increasingly multimodal models and products behave safely across these experiences. We develop the research, training methods, and evaluations needed to make these experiences safe. Our work sits at the frontier of responsibly deploying powerful AI, in close partnership with Personal AGI, io, model training, and product teams. About the Role As a Researcher on the Chat and Multimodal Safety team, you will help shape how frontier models perceive and reason the world, and translate that understanding into safe behavior. We’re looking for people who combine deep technical expertise with strong safety judgment. Strong candidates often bridge perception and language: they may have built vision-language models, worked on modality fusion or image encoders, developed multimodal post-training or evaluations, or advanced safety for image, video, or audio systems. 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 will: Define and advance multimodal safety research for text, vision, and audio, connecting perception and semantic understanding to safe model behavior. Build training and evaluation methods for VLMs, including post-training, safety evals, and interventions that help models respond safely and appropriately in varied contexts. Collaborate closely with Personal AGI, Consumer Devices, and product/model teams to translate research into safer ambient, embedded, and personalized multimodal experiences. You might thrive in this role if you:

About the Team The CoT Monitorability team at OpenAI studies whether and when the chain-of-thought of frontier reasoning models is monitorable enough to support scalable oversight. We study how to measure monitorability , which training mechanisms affect monitorability, and speculative methods to improve monitorability. While we mostly focus on CoT monitorability at the moment, we care more generally about any form of monitorability, auditing methods, and improving alignment. We were the first to show that chain-of-thought monitoring can be a practical additional safety mechanism, and today our monitoring systems are actively used on OpenAI’s largest RL training runs to detect misbehavior. The issues we surface are then used to help improve our reward functions, environments, etc (without directly training against a CoT monitor). Our work sits in Alignment and intersects with model training, alignment evaluations, monitoring, and frontier-risk research.We care most about monitorability where the stakes are high, and about preserving useful oversight signals as models become more capable. About the Role We’re looking for a researcher with strong empirical ML expertise and a deep interest in model behavior, alignment, or interpretability. Direct chain-of-thought interpretability experience is welcome but not required; strong candidates may come from broader interpretability, alignment, model training, or investigative model-behavior work. As a researcher on the Alignment team, you will design and run experiments that improve our understanding of model monitorability. You will investigate how training interventions across the model-development pipeline influence whether reasoning remains legible, build evaluations that make those questions measurable, and help translate findings into practical oversight and training recommendations. You may also help develop new monitoring models or methods and apply them to OpenAI’s largest training runs. This role is especially well

About the Team Our 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. Within Safety Systems, the Model Policy team works to ensure that increasingly capable models behave safely and reliably in real-world environments. We investigate emerging model failures, define the behavior models should exhibit instead, and develop the data, evaluations, monitoring, and safeguards needed to improve and validate that behavior. Our work connects alignment research with the practical challenges of training and deploying frontier models. About the Role In this role, you will shape how OpenAI understands and addresses real-world risks that emerge from model misalignment as models become more autonomous and operate over longer horizons. You will investigate how misaligned behavior emerges across extended trajectories - including when models persist toward the wrong objective, take unsafe shortcuts, lose track of instructions, exploit weaknesses in their environment, or circumvent constraints - and translate these insights into behavioral policies, evaluations, monitoring, and safeguards. This role is ideal for someone who wants to turn alignment and safety concerns into concrete, empirically grounded improvements to frontier AI systems. Your Responsibilities: Identify vulnerabilities that emerge as models interact with tools, data, and external systems, and translate them into model- and system-level safeguards. Develop threat models and empirical frameworks for understanding harmful outcomes from misaligned behavior. Build frameworks for understanding harmful outcomes arising from model misalignment. Identify the underlying behaviors and system conditions that drive those outcomes. Turn findings into policy frameworks, evaluation criteria, online measurement and safeguards. Develop human data campaigns and gold sets to ground measurement and evaluation of eme

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by ensuring teams have access to high-quality labeled data. This is facilitated by a comprehensive labeling stack featuring a workflow execution framework, supporting infrastructure, and a suite of data annotation tools. Nuro’s autonomy stack utilizes an industry-leading sensor suite. Our tools must handle the efficient processing and annotation of millions of points of sensor data. Our labeling infrastructure supports millions of scenes weekly. The platform team’s mission is to make labeled data accessible for all of our users. The system must be reliable and scalable. This includes everything from request submission to p

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role The mandate of the learned behavior team is to use advanced machine learning techniques to accelerate software progress. In this role, you will work closely with the software vertical teams to understand their pain points and explore novel and advanced machine learning methods to solve practical real-world challenging problems. To name a few, using self-supervised learning to learn robust representations, exploring techniques for out-of-distribution detection to solve long tail problems, adjusting reinforcement learning techniques for motion planning, working on trajectory prediction and motion planning, investigating the robustness of models to mitigate uncertainties, or trying to build an end-to-end driving system. If you love solving challenging new problems with a mindset of deriving practical solutions to eventually be used on the vehicle, come join us! About the Work Work on scalable machine learning based planning and pre

About The Team The Data Understanding team is responsible for creating the high quality datasets and their quantized representation for OpenAI. This includes synthesizing data, building VQ representations, and processing, filtering, deduplication, quality control, and tokenization so it can be used effectively in big model training runs. About The Role We're looking to advance how OpenAI builds and understands pretraining data at scale. You'll treat data quality and curation as core research problems: developing new methods to select, combine, and transform data; creating datasets that improve model capabilities; and designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior. You'll work closely with frontier models and web-scale data to build evidence for which approaches work and why, then translate successful research into scalable data processing pipelines We Expect You To Have a strong track record of new or improved ML ideas, through publications, projects, or applied research. Own and drive a research agenda, from choosing the right problems to carrying long-running work through to impact. Be excited by OpenAI’s empirical, collaborative approach to research. Nice To Have Thoughtfulness about AI’s impact, including privacy, provenance, and data quality. Experience building high-performance deep learning or large-scale data processing systems. 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 that form the full spectrum of humanity. We are an equal opportunity employer

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. The Opportunity Okta is building an AI-enabled demand engine that uses signals, automation, and intelligent engagement to identify, qualify, route, and progress the right buyers more effectively. The AI Journey Operations Manager will own the business strategy, qualification framework, routing logic, operating model, and performance optimization for AI-assisted xDR capabilities across the Global Demand Center. This role is not primarily a platform administration or workflow-build role. It is a senior, cross-functional capability-owner role responsible for determining how AI-assisted qualification and engagement should operate, how it connects to human xDR and Sales follow-up, and how it contributes to measurable funnel and pipeline outcomes. The ideal candidate combines AI-enabled GTM experience, xDR/Sales process knowledge, demand generation, revenue operations, and journey strategy. They can translate business priorities into qualification logic, routing rules, AI engagement workflows, handoff requirements, measurement frameworks, and ongoing optimization plans. What You’ll Do Own AI xDR qualification strategy Define qualification frameworks for AI-assisted engagement across priority inbound, account-based, product-led, customer, and signal-triggered motions. Establish the fit, behavior, intent, engagement, and business-context criteria used to determine whether a lead or account should be engaged, progressed, routed, recycled, or suppressed. Partn

About the Team The Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and 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 Safety Research team aims to fundamentally advance our capabilities for precisely implementing robust, safe behavior in AI models, and to leverage these advances to make OpenAI’s deployed models safe and beneficial. This requires a breadth of new ML research to address the growing set of safety challenges as AI becomes more powerful and used in more settings. Key focus areas include how to enforce nuanced safety policies without trading off helpfulness and capabilities, how to make the model robust to adversaries, how to address privacy and security risks, and how to make the model trustworthy in safety-critical domains. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. About the Role OpenAI is seeking a senior researcher with passion for AI safety and experience in safety research. Your role will set directions for research to enable and empower safe AGI and work on research projects to make our AI systems safer, more aligned and more robust to adversarial or malicious use cases. You will play a critical role in shaping how a safe AI system should look like in the future at OpenAI, making a significant impact on our mission to build and deploy safe AGI. In this role, you will: Conduct state-of-the-art research on AI safety topics such as RLHF, adversarial training, robustness, and more. Implement new methods in OpenAI’s core model training and launch safety improvements in OpenAI’s products. Set the research directions and strategies to make our AI systems safer, more aligned and more robust. Coordinate and collaborate with cross-functional team

About The Team The Data Understanding team is responsible for creating the high quality datasets and their quantized representation for OpenAI. This includes synthesizing data, building VQ representations, and processing, filtering, deduplication, quality control, and tokenization so it can be used effectively in big model training runs. About The Role We're looking to advance how OpenAI builds and understands pretraining data at scale. You'll treat data quality and curation as core research problems: developing new methods to select, combine, and transform data; creating datasets that improve model capabilities; and designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior. You'll work closely with frontier models and web-scale data to build evidence for which approaches work and why, then translate successful research into scalable data processing pipelines We Expect You To Have a strong track record of new or improved ML ideas, through publications, projects, or applied research. Own and drive a research agenda, from choosing the right problems to carrying long-running work through to impact. Be excited by OpenAI’s empirical, collaborative approach to research. Nice To Have Thoughtfulness about AI’s impact, including privacy, provenance, and data quality. Experience building high-performance deep learning or large-scale data processing systems. 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 that form the full spectrum of humanity. We are an equal opportunity employer

Squarespace is looking for an analytical, curious thinker to help build out the economics of our growing AI product portfolio. This role sits at the intersection of commercial strategy and emerging technology — supporting the pricing, margin analysis, and packaging work that shapes how Squarespace monetizes AI-powered features for customers and manages AI costs internally. You will join a centralized Commercial Strategy team that drives revenue growth across the platform by optimizing monetization models and packaging for Squarespace's product suite. The team works across pricing, packaging & promotions, purchase behavior analysis, and feature monetization — aligning price with customer value and unlocking incremental ARPU and retention. In this role, you will support the team's AI pricing and margin workstreams: building financial models, analyzing usage and cost data, researching the competitive AI landscape, and partnering with Product and Billing to bring pricing decisions to life. You will report to Commercial Lead and work from our NYC office 2–3 days per week. What You'll Do… Cost & Margin Analysis Analyze AI usage data — including API costs, token consumption, and inference spend — to build a clear picture of margin across customer-facing features and internal tooling. Build and maintain financial models that translate AI usage into credit equivalents, helping the team track unit economics across model providers and feature surfaces. Support the development of model routing frameworks by modeling the cost and quality tradeoffs of different routing configurations. Pricing & Packaging Support Support the design and evaluation of customer-facing credit and usage-based pricing models, including tier structures, usage limits, and overage mechanics. Help translate pricing strategy into concrete SKU and entitlement definitions, working with Product and Billing to ensure commercial intent is correctly implemented. Assist in structuring promotional offers

About the Team DoorDash Labs is a team within DoorDash building autonomous delivery robots and other autonomy solutions from the ground up for DoorDash's core delivery platform. If you have a passion for applying robotics solutions to a service loved by millions of people, then we want to talk to you! About the Role As Team Lead, Autonomy Tech Support, you will lead the day-to-day execution and development of the Mexico City Autonomy Tech Support team while maintaining a strong understanding of its technical workflows. You will set a high bar for troubleshooting, escalation quality, documentation, and operational readiness as the autonomous delivery fleet continues to scale. You will report into the Manager, Autonomy Tech Support on our Autonomy Tech Support team in our DoorDash Labs organization. Work model: 100% in-office in Coyoacán, Mexico City, Open for T4 levels, Across all LOBS. You’re excited about this opportunity because you will… Lead the day-to-day execution of the CDMX ATS team, including coverage, workload, priorities, and high-impact fleet issues. Coach and develop ATS Specialists through regular feedback, technical coaching, and hands-on support. Maintain a high bar across troubleshooting, escalation quality, Failure Mode execution, documentation, and cross-functional communication. Serve as the first leadership escalation point in CDMX for complex or high-urgency issues. Be a first line of defense for potential software regressions and emerging fleet-level trends, ensuring abnormal behavior is identified, validated, and escalated quickly. Lead AI adoption, integration, and enablement across the CDMX ATS team, ensuring AI capabilities are effectively incorporated into day-to-day workflows while identifying opportunities to improve troubleshooting, decision-making, and team efficiency. Partner with Operational Intelligence and Operational Reliability to ensure the team has the tools, systems, and processes needed to operate effectively and effic