New Restaurant Models Strategy And Operations Director in San Francisco

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborate with cross-functional teams to define, design, and deliver new features. Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades. Present technical information to teams and stakeholders, providing

About the role: We are looking for a talented and experienced University Recruiter, Contractor based in San Francisco to join our team and participate in the hiring process from beginning to end. You’ll be working directly with our hiring partners, recruiters and candidates to create a personal and positive recruiting experience. You will: Support Scale’s University Recruiting strategy: assist with the hiring process for our Internship and New Grad programs. Manage the full-cycle recruiting strategy and process including, but not limited to, sourcing, screening, interview process, closing and more. Understand what makes a great profile for our University roles and drive strategic sourcing and candidate engagement strategies to build strong and diverse pools of candidates. Cultivate relationships with students across partnered universities and develop positive relationships with faculty, staff, and student organizations as part of a well-researched recruiting strategy. Collaborate closely with various leaders across the organization in understanding and meeting their intern and new grad hiring needs. Work from the San Francisco office 3x per week. You have: 4+ years of full-cycle university recruiting experience in a fast-paced, high-growth environment, managing high-volume hiring. Experience in managing/leading all aspects of recruiting strategy for hard-to-fill, competitive positions. Passionate about campus recruiting and strong believer in bringing early career talent to Scale. Excellent verbal and written communication skills. Excellent negotiation tactics, and market knowledge. You have the ability to work with multiple departments, including remote teams, and build strong personal networks across our complex organization. Proven ability in attracting and sourcing both passive and active candidates (aka future graduates) across Bachelors, Masters, and PhD programs. Utilizing data to perform analysis, garner insights, and set goals. A flexible, adapt

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability. You will: Own large new areas within our product Work across backend, frontend, and interacting with LLMs and ML models Deliver experiments at a high velocity and level of quality to engage our customers Work across the entire product lifecycle from conceptualization through production Be able, and willing, to multi-task and learn new technologies quickly Ideally you'd have: 7+ years of full-time engineering experience, post-graduation Experience scaling products at hyper growth startups Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies Proficient in Python or Javascript/Typescript, and SQL Experience with Kubernetes Experience with major cloud providers (AWS, Azure, GCP) Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities. In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models. You will: Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities. Design and experiment new approaches to preference optimization. Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning. Excellent written and verbal communication skills Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined du

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will support the design and development of shared platforms used across Scale. This includes designing our foundational data platforms and lifecycle, architecting Scale’s core cloud infrastructure and orchestration stack, and redefining how engineers develop, build, test, and deploy software at Scale. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborating with cross-functional teams to define, design, and deliver new features. Proactively identifying opportunities for, and driving improvements to, current p

Scale Labs, Research Scientist — Safety Post Training As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer, and better understood by researchers and policymakers.. For example, you might: Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties; Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations; Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches. A track record of published research in machine learning, particularly in generati

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Identity Engineering team. In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems. Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect). Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access cont

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Identity Engineering team. In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems. Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect). Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access cont

Scale GP is Scale's enterprise Generative AI platform—APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises, helping teams turn complex data and models into reliable, production-ready AI systems. We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. What we run on is what we sell. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions. If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you. What You’ll Do Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area Sample surfaces - Accounting Agents, Finance Copilots, GTM Agents, Agentic Experimentation Platforms Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows Ship quickly through tight experimentation loops while maintaining high quality and reliability Adapt across the stack and learn new tools as needed to solve real problems end-to-end Ideal Experience 3+ years of full-tim

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will support the design and development of shared platforms used across Scale. This includes designing our foundational data platforms and lifecycle, architecting Scale’s core cloud infrastructure and orchestration stack, and redefining how engineers develop, build, test, and deploy software at Scale. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborating with cross-functional teams to define, design, and deliver new features. Proactively identifying opportunities for, and driving improvements to, current p

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Senior Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterpri

Scale Labs, Research Scientist — Frontier Risk Evaluations As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities; Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems; Publish evaluation methodologies and write technical reports for policymakers. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes. A track record of published research in m

Location - Hybrid This is a hybrid role based out of our San Francisco, Corporate Headquarter office, 3 days in the office, 2 days work from home. About Us Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at http://saucelabs.com . Release Assurance at the Speed of AI | Meet the new Sauce Labs The Role The Senior GTM Strategy & Planning Analyst will play a key role in supporting go-to-market (GTM) strategy through sales forecasting, churn forecasting, revenue analytics, and capacity planning across the GTM teams. This role will partner closely with Sales, Marketing, Customer Success, and Finance to align GTM initiatives with company growth objectives and contribute to executive-level insights for board presentations. Responsibilities GTM Board Materials: Support preparation of sales forecasting, churn forecasting, and revenue insight materials for executive and board review, ensuring data accuracy and alignment with company strategy. Executive Reporting: Help develop executive-level reporting materials, including presentations that translate complex data into clear, actionable insights. Forecasting: Build and maintain sales forecasting models in partnership with Sales Leadership, modeling pipeline generation, deal velocity, close rates, and other key metrics. Build predictive models to forecast customer churn, leveraging historical data and customer behavior insights to inform retention and renewal strategies. GTM Capacity Planning: Build and maintain capacity models to help ensure GTM t

About the Team Our US Marketplace team is responsible for growing the DoorDash business in the US, helping customers to discover & order what’s best in their city, merchants to grow their sales, and dashers to find flexible work. About the Role The US Marketplace team is looking for a motivated and hands-on Associate Manager to join the Strategy & Operations Growth and Markets team. You will own a portfolio of projects aimed at growing the core Marketplace business, working closely with DoorDash’s product, marketing, finance, and analytics teams.You’ll have the opportunity to help grow strategic markets, identify new growth bets, incubate 0-1 experiments, and execute on a roadmap to drive growth. You’re excited about this opportunity because you will… Build and own. You’ll have your fingerprints all over DoorDash’s next wave of growth. You’ll do whatever it takes to launch, get 1% better every day, and scale something completely new, from high-level strategy down to the nitty-gritty operational details. Incubate. You’ll take bets that don’t have an owner yet, scope them, test them, and either graduate them into funded programs or kill them with a clear, well-argued recommendation. Strategize. From first principles, you’ll develop the optimal strategy for growing segments and launching bets that no existing playbook covers. Experiment. You’ll turn strategies into operational experiments that create measurable, actionable outcomes, then use those results to shape the larger implementation plan. Analyze. You’ll pull your own data, assess the operational and financial impact of your decisions, and iterate based on what you find. Lead and collaborate. You’ll work with cross-functional partners across product, analytics, marketing, sales, and finance to get your initiatives resourced and shipped. We’re excited about you because… You have 4+ years of experience in a problem-solving role: consulting, tech, banking, or similar. Experience taking something