AI Architect - People Products

<p class="p1">Toast is driven by building the restaurant platform that helps restaurants adapt, take control, and get back to what&nbsp;they do best: building the businesses they love.&nbsp;</p> <p class="p1">We're looking for an AI Architect to design and lead the implementation of our AI infrastructure for People Products and define the overall technical strategy for the entire People Products ecosystem. This crucial role demands deep technical expertise and strategic vision. You will lead the creation of the People team’s AI architecture, utilizing various frameworks and deployment models to devise and execute an effective AI strategy.</p> <p class="p1">You’ll be part of our People and Places team whose ambition is that our People Experience is a key differentiator and reason for candidates to choose Toast and employees to stay at Toast, as much as our Products are today. Our People Products team goal is to amplify the People team’s ambition by developing AI-powered conversational self-service products to deliver personalized employee experiences and insights in the flow of work through cloud-based scalable, integrated, compliant solutions. You will partner closely with Product Managers, Third-Party Tools (3PT) Specialists, Engineering, Data Science, and UX.&nbsp;</p> <p class="p1">In this high-impact role, you will be the creative and technical force who converts employee needs into scalable and effective AI solutions. You'll focus on optimizing AI technology integrations to enhance operational efficiency, boost productivity, and drive innovation - all while adhering to the highest ethical standards. You'll join a team that is redefining how a mission-driven company operates and scales its most important asset: its people.</p> <p><strong>A day in the life (Responsibilities)&nbsp;</strong></p> <p class="p1">As the AI Architect, you are the lead technical planner and decision-maker, and a key contributor to the development&nbsp;of our AI solutions. Your responsibilities will extend beyond high-level design; you will actively participate in the&nbsp;engineering lifecycle to build, prototype, and validate our core AI components. This role requires hands-on&nbsp;development to establish technical standards and build the foundational systems that accelerate delivery.</p> <p class="p2">Your primary focus areas will include:</p> <ul> <li class="p1">Strategic Planning: Partner with Product Management to define and articulate the comprehensive AI&nbsp;technical strategy and roadmaps, ensuring precise alignment with overarching company objectives.</li> <li class="p2">Systems Architecture: Architect end-to-end AI systems, including data pipelines, model training and&nbsp;deployment infrastructure, and APIs. All designs must prioritize scalability, security, high availability, fault&nbsp;tolerance, and operational efficiency.</li> <li class="p2">Platform-First Mindset: Design and build core AI services and infrastructure as a reusable platform, enabling&nbsp;engineering and data science teams to rapidly build and deploy new 0-1 AI-powered features.</li> <li class="p2">Enterprise &amp; Ecosystem Architecture: Lead the integration of AI tools with existing enterprise systems (e.g.,&nbsp;HR platforms, intranet, collaboration tools) and provide architectural guidance for the broader People</li> <li class="p2">Products ecosystem, ensuring all components (AI and non-AI) work together as a cohesive, scalable, and&nbsp;secure platform.</li> <li class="p1">Requirements Translation: Translate complex business requirements and stakeholder goals into clear,&nbsp;actionable technical specifications.</li> <li class="p3">Technology Evaluation: Conduct thorough research and evaluation to select optimal tools, platforms, and&nbsp;frameworks. This involves balancing performance, cost, and long-term compatibility.</li> <li class="p2">Performance Optimization: Establish and manage processes for the continuous monitoring and optimization&nbsp;of AI systems to enhance accuracy, performance, and cost-effectiveness.</li> <li class="p2">Ethical AI &amp; Governance: Champion and enforce ethical AI principles. You will be responsible for ensuring all&nbsp;solutions are fair, transparent, and rigorously protect employee data privacy in accordance with compliance&nbsp;standards. This includes proactively collaborating with security and risk leaders to foresee and mitigate&nbsp;potential risks associated with AI deployment.</li> <li class="p2">Technical Project Leadership: Oversee the technical execution of AI projects from conception to completion, ensuring alignment with the defined architecture and roadmap, and coordinating with cross-functional teams&nbsp;to meet delivery milestones.</li> <li class="p2">Technical Leadership: Mentor and provide technical guidance to AI professionals, fostering a creative,&nbsp;collaborative, and high-performance team environment.</li> <li class="p2">MLOps Standardization: Define and champion best practices for the full MLOps lifecycle, including version&nbsp;control, automated testing, and reproducible deployment pipelines.</li> <li class="p2">Stakeholder Communication: Clearly and effectively communicate and advocate for complex AI concepts,&nbsp;architectural decisions, risks, and opportunities to non-technical stakeholders and senior leadership to inform&nbsp;strategic-decision making.</li> <li class="p2">Cross-Functional Collaboration: Work in close partnership with data scientists, software engineers,&nbsp;designers, and product managers to co-develop innovative solutions on our AI platform.</li> <li class="p2">Continuous Research: Maintain expert-level knowledge of emerging trends, academic research, and&nbsp;advancements in AI/ML to identify and capitalize on new opportunities.</li> </ul> <p><strong>What you'll need to thrive (Requirements)</strong></p> <ul> <li class="p1">Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, IT ,&nbsp;Software Engineering, or a related technical field. A PhD may be beneficial.</li> <li class="p3">Experience: At least 10 years of demonstrable experience designing, building, and leading complex technical&nbsp;projects, with a deep specialization in AI/ML from conception through to production.</li> <li class="p4">Technical Acumen: Expert-level understanding of fundamental computer science concepts, including data&nbsp;structures, machine learning algorithms, deep learning frameworks, statistics, and advanced data analysis.</li> <li class="p4">Professional Background: Prior experience in roles such as AI Engineer, Software Engineer, Cloud Engineer, or&nbsp;Data Scientist is highly valued.</li> <li class="p4">Cloud Proficiency: Deep comfort with at least one major cloud platform (AWS, Google Cloud, or Azure) and its&nbsp;associated AI/ML services and infrastructure.</li> <li class="p3">Leadership: Capability to lead, mentor, and manage cross-functional teams, driving AI initiatives and&nbsp;influencing senior leadership.</li> <li class="p4"><span class="s1">Communication: </span>Exceptional written and verbal communication skills, with the ability to <span class="s1">effectively </span>collaborate with stakeholders (technical and non-technical) and present complex findings.</li> <li class="p4">Problem-Solving: Strong analytical and problem-solving capabilities, with a proven capacity for addressing&nbsp;ambiguous challenges and considering long-term strategic implications.</li> <li class="p4">Business Acumen: Demonstrated ability to translate high-level business objectives into robust, scalable&nbsp;technical architectures.</li> <li class="p4">Continuous Learning: A strong commitment to continuous professional development and staying abreast of&nbsp;the rapidly evolving AI landscape.<span class="s3">●</span></li> <li class="p4">Attention to Detail: Meticulous attention to detail, particularly regarding system reliability, security, and data governance.</li> </ul> <p>(Nice-to-Haves)</p> <ul> <li class="p3">Generative AI Specialization: Specific, hands-on experience with Generative AI, Large Language Models&nbsp;(LLMs), and agentic AI systems.</li> <li class="p3">Big Data Expertise: Familiarity with big data processing tools like Hadoop, Spark, and Kafka.</li> <li class="p4"><span class="s1">Platforms: </span>Experience building internal developer platforms, SDKs, or tools that serve an engineering and&nbsp;data science audience.</li> <li class="p3">ML Operations (MLOps): Deep knowledge of the workflow and pipeline architectures of ML and deep learning <span class="s1">workloads, </span>and practical application of MLOps principles for managing automated ML workflows.</li> <li class="p3">Software Engineering: A robust background in software engineering and DevOps, with a fo