APIs AI Jobs

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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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Build and maintain back end services that handle model inference and user requests, design systems to manage requests, sessions, and streaming responses, implement reliability mechanisms such as rate limiting, retries, and graceful failure, build authentication and access controls for public usage, design systems for logging, telemetry, and evaluation signals, improve latency, throughput, and reliability of model serving, integrate new model checkpoints into the production system, and work closely with training and infrastructure engineers to deploy and operate the model. The role involves working inside production systems including logs, traces, performance profiles, and deployment pipelines to ensure the system stays up, fast, and behaves predictably under load.

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Build and own autonomous, customer-facing agentic systems that directly drive Revenue, Pipeline, and Marketing efficiency. Own end-to-end product execution, from early prototypes to reliable production systems with strong instrumentation and evaluations. Work across the full stack, including APIs, orchestration, data flows, frontend experiences, and deployment. Partner closely with marketing, demand gen, and enterprise sales stakeholders to define success metrics and functional requirements. Apply OpenAI models and tooling in novel ways, making informed tradeoffs between models, platforms, and architectures. Continuously iterate based on live usage, agent behavior, and performance data.

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Lead and grow a high-performing team of 8–9 engineers building reinforcement learning environments. Manage, mentor, and develop senior engineers and future engineering leaders. Partner closely with research, product, and operations teams to define roadmap and execution priorities. Drive technical architecture for scalable, reliable, and extensible environment systems. Build plug-and-play environments that integrate seamlessly with model training pipelines. Balance platform rigor with operational complexity and data quality requirements. Establish engineering best practices around reliability, observability, and performance. Foster a culture of ownership, velocity, and high technical standards.

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As a Product Engineer on the Marketing Innovation team, you will build and own autonomous, customer-facing agentic systems that interface directly with enterprise customers, prospects, and revenue-critical workflows. You will partner closely with functional leaders across scaled revenue, demand generation, and marketing to understand desired outcomes and translate those needs into production-grade systems. Responsibilities include building autonomous and semi-autonomous customer-facing and internal agentic systems that drive revenue, pipeline, and marketing efficiency; owning end-to-end product execution from prototypes to reliable production systems with strong instrumentation and evaluations; working across the full stack including APIs, orchestration, data flows, frontend experiences, and deployment; partnering closely with marketing, demand generation, and enterprise sales stakeholders to define success metrics and functional requirements; applying OpenAI models and tooling innovatively, making informed tradeoffs between models, platforms, and architectures; and continuously iterating based on live usage, agent behavior, and performance data.

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The Senior Backend Engineer, Learn (Core Systems) is responsible for redesigning existing components to support enterprise-scale workloads, analyzing and resolving bottlenecks in storage, query performance, APIs, and data models, leading migrations away from legacy implementations to sustainable replacements, improving reliability and efficiency of APIs and integrations for internal and external clients, driving technical projects from definition to delivery with Product Managers and other teams, maintaining a long-term view of system health and architecture, and sharing technical knowledge, reviewing designs, and setting best practices for backend and systems design. The Senior Backend Engineer, Search is responsible for architecting and scaling search infrastructure to billions of documents in multi-tenant environments, designing hybrid search combining keyword search with semantic understanding and vector search, building ranking and personalization systems that learn from user behavior, collaborating with AI engineers to integrate large language models into the search pipeline and build retrieval augmented systems, optimizing search performance across query parsing, index design, and distributed architecture, leading development of search observability and quality frameworks with clear metrics and monitoring, and working closely with product and design to shape the future of knowledge discovery at Sana.

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As a Backend Platform Engineer at Harvey, you will build and operate the backend platform that supports all company services, designing and implementing shared frameworks and libraries to abstract common concerns and improve developer experience. Responsibilities include developing and maintaining internal backend frameworks and libraries for capabilities such as API routing, service lifecycle management, caching, messaging, and error handling, creating and improving APIs, service templates, and versioned interfaces, and championing modern backend architecture patterns like asyncio and streaming data processing. You will design Harvey-specific abstractions and domain-specific frameworks covering cross-cutting concerns and areas like data governance and event processing. Embedding reliability and observability through tracing, metrics, and automated tests to ensure robustness and ease of monitoring is required. Collaboration with the Model Infrastructure team, Developer Experience and Infrastructure teams to integrate platform components, handle GenAI-native application challenges, and gather feedback from product engineering teams is part of the role. Evangelizing best practices and providing strong defaults and clear documentation to facilitate fast and confident development by product teams is also expected.

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You will design and build backend and infrastructure systems for OpenAI’s monetization and ads stack, emphasizing reliability, privacy, security, and large-scale performance. You’ll develop APIs and platforms, drive 0→1 infrastructure projects, and collaborate cross-functionally with Product, Research, and Design teams.

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The Enterprise Sales Manager, Financial Services at OpenAI will recruit, develop, and lead a team of Account Directors focusing on acquiring and growing enterprise Financial Services clients. This role involves shaping go-to-market strategy, creating sales playbooks, guiding complex, technical sales opportunities, and collaborating with product, marketing, finance, and technical success teams.