AI Program Manager Jobs

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Lead, hire, and mentor a high-performing team of AI Deployment Engineers supporting Codex customers across strategic accounts. Own the operating model and engagement strategy for Codex deployment efforts, ensuring customers successfully move from pilot to production adoption. Guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures. Act as the senior technical escalation point for complex customer implementations and deployment challenges. Partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities. Help establish repeatable deployment playbooks, technical patterns, and best practices that enable scaled adoption of AI coding tools. Coach engineers to operate as trusted advisors to engineering leadership and executive stakeholders. Synthesize insights from customer deployments and translate them into actionable feedback for internal teams. Champion safe, reliable, and effective adoption of AI-powered development workflows across industries.

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As Head of EPD Systems & AI Transformation at Vanta, you will lead a strategic transformation and team development by defining, leading, and delivering a multi-quarter roadmap to transform Engineering, Product, and Design through automation and AI workflows, growing a small team responsible for EPD systems, programs, and the agentic transformation, and serving as a strategic partner to the CPO and EPD Leadership Team by identifying systemic risks and opportunities and implementing interventions. You will design and build an AI-native EPD operating system that encompasses end-to-end information flow across core systems to minimize time spent reconciling data, automate status reporting with team and executive rollups, develop agents that help teams retrieve answers, produce drafts, trigger workflow actions, and maintain data hygiene, and establish continuous evaluation and improvement practices including defining success metrics, running phased rollouts, and measuring adoption. Additionally, you will transform customer and GTM feedback loops by automating ingestion and synthesis of feedback at scale, partnering closely with GTM teams to ensure feedback is actionable and complete the communication loop with customers, and create guardrails for AI-enabled workflows addressing privacy, data handling, reliability, auditability, and human-in-the-loop expectations in partnership with Security and IT.

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Contribute to design, build, and maintain services that power AI-driven applications, ensuring scalability and performance. Develop APIs and microservices that facilitate seamless integration between cloud-based AI models and edge devices. Optimize data pipelines and storage solutions for real-time AI inference and processing. Work closely with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions. Build and optimize an agent orchestration runtime that enables tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems. Support the implementation of security and privacy best practices for distributed AI systems.

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The AI Implementation Manager is responsible for owning the delivery and stabilization of Ema’s agentic AI solutions from commitment through production rollout and steady state. This includes end-to-end delivery ownership, ensuring solutions align with Ema’s architecture and platform capabilities. They develop a deep understanding of customer business processes and translate workflows into feasible agentic AI workflows. The role involves providing technical oversight focused on delivery without being the primary builder, anticipating potential implementation issues such as integration, data quality, scale, and edge cases. The manager acts as the primary delivery point of contact for customer business and IT stakeholders and coordinates across Engineering, Product, Data, Infrastructure, and Value Engineering teams. They coach stakeholders and teams during high-stress phases to reduce chaos, communicate delivery progress, risks, and decisions to all audiences, and track success through adoption and outcome-adjacent metrics. Additionally, they provide day-to-day delivery leadership and mentorship to promote shared standards, clear ownership, and delivery discipline.

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The AI Implementation Manager is responsible for owning the delivery and stabilization of Ema's agentic AI solutions from commitment through production rollout and steady state. Responsibilities include end-to-end AI delivery ownership, ensuring solutions align with Ema's agentic architecture and platform capabilities, developing a deep understanding of customer business processes to translate workflows into feasible agentic AI workflows, providing delivery-focused technical oversight to anticipate implementation issues, acting as the primary delivery point of contact for customer business and IT stakeholders, coordinating across Engineering, Product, Data, Infrastructure, and Value Engineering teams, managing delivery under pressure by coaching stakeholders, communicating delivery progress, risks, and decisions clearly, tracking success through adoption signals and outcome-adjacent metrics, and providing day-to-day delivery leadership and mentorship to promote shared standards and delivery discipline.

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Lead the team responsible for the AI/ML Stack infrastructure that bridges ML research and large-scale production, evolving the stack to meet scalability needs in ML training and inference workloads. Develop and execute the long-term vision and roadmap for the MLOps team to support ML development and deployment across business units, balancing short-term tactical deliveries and long-term architectural transformation. Manage and mentor a team of 6-7+ engineers, allocate resources strategically to support existing services and strategic initiatives. Collaborate across machine learning, data science, product engineering, and infrastructure teams to identify and address bottlenecks and facilitate deployment of new solutions. Architect compute and storage pipelines to manage large datasets without fragmentation or latency. Modernize the AI product inference stack to support significant growth in AI runs globally. Work with Site Reliability Engineering to establish comprehensive system observability metrics. Conduct build vs. buy assessments and technology stack refresh audits to benchmark and ensure best toolsets are in use.

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The Chief Technology Officer is responsible for defining the long-term architecture for A1's AI systems, infrastructure, and developer platform, evaluating trade-offs between speed of iteration and long-term system design, and ensuring systems are designed for scalability, reliability, and long-term evolution. They guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO works with engineers to translate product direction into clear technical execution, helps structure engineering workstreams and maintain team alignment on priorities, maintains high engineering standards while encouraging shipping, and establishes engineering culture, development practices, and technical standards across the company. They build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, and ensure technical workstreams move forward smoothly across teams and locations. The CTO works closely with product, research, and leadership teams and helps resolve cross-team technical and execution challenges.

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The Chief Technology Officer will define the long-term architecture for A1’s AI systems, infrastructure, and developer platform, evaluate trade-offs between speed of iteration and long-term system design, and ensure systems are designed for scalability, reliability, and long-term evolution. They will guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO will work with engineers to translate product direction into clear technical execution, help structure engineering workstreams and keep teams aligned on priorities, maintain high engineering standards while focusing on shipping, and establish engineering culture, development practices, and technical standards. Additionally, they will build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, work closely with product, research, and leadership teams, ensure technical workstreams move forward smoothly across teams and locations, and help resolve cross-team technical and execution challenges.

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together’s inference stack, including kernel backends, speculative decoding like ATLAS, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines, optimizing algorithms and systems for efficiency where inference constitutes the majority of the cost. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, and speculative decoding to reduce large-scale rollout collection and evaluation costs. Use these pipelines to train, evaluate, and iterate on frontier models atop the inference stack. Co-design algorithms and infrastructure for tightly coupled objectives, rollout collection, and evaluation with efficient inference, and identify bottlenecks across training engines, inference engines, data pipelines, and user-facing layers. Conduct ablations and scale-up experiments to analyze trade-offs among model quality, latency, throughput, and cost, using insights to inform model, RL, and system design. Profile, debug, and optimize inference and post-training services under production workloads. Lead roadmap efforts that require engine modifications including changes to kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training and mentoring engineers and researchers in full-stack ML systems work and performance engineering.

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The Senior Engineering Manager leads a core product and platform engineering team responsible for building systems that integrate human expertise into AI development workflows. The team owns critical infrastructure connecting talent networks, data operations, and research needs into scalable, reliable, and high-quality platforms. The role involves leading, hiring, and developing a high-performing engineering team, owning roadmap and execution in close partnership with Product, Research, and Operations, driving architecture and technical strategy for scalable and extensible systems, building modular platforms to enable new domains and workflows to launch quickly, raising engineering quality across reliability, observability, performance, and data integrity, and fostering a culture of ownership, velocity, and strong engineering fundamentals in a fast-moving, ambiguity-heavy environment.