AI Workflow & Operations, Revenue Strategy & Operations

ID: 9396

Type: Full-time

Category: Others

Company Name: Rockset

Location: California (USA) - California - United States

Education Level: Senior (5-10 years)

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Job Description

About the Team

OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals together to accomplish this.

In pursuit of this mission, our Go To Market (GTM) team helps customers deploy and realize value from OpenAI’s products across their businesses. The team spans Sales, Solutions, Support, Marketing, Partnerships, and Operations working together to scale AI adoption globally.

Within GTM, the Strategy & Operations organization builds the operational systems, workflows, analytics, and infrastructure that power the business. As AI rapidly transforms how work gets done, we are building a new class of AI-native internal tooling and workflows to accelerate productivity, improve decision-making, and scale execution across Sales, Solutions, and Revenue Operations.

About the Role

We are looking for an AI Workflow & Operations [engineer] to design and build AI-native workflows, automations, and internal tools that accelerate OpenAI’s GTM organization.

This role sits at the intersection of Revenue Operations, AI product development, systems design, and workflow engineering. You will work directly with Sales, Solutions, RevOps, and GTM leadership to identify high-leverage operational problems and rapidly prototype and deploy AI-powered solutions using OpenAI’s products, APIs, Codex, and automation platforms.

You will act as both a builder and operator — deeply understanding GTM workflows while designing practical AI systems that improve field productivity, operational scale, forecasting quality, account planning, pipeline management, customer execution, and internal decision-making.

This role is ideal for someone who enjoys translating ambiguous business problems into working AI-native systems and wants to help define how modern GTM organizations operate in an AI-first world.

In this role, you will:

Identify opportunities where AI can improve the quality, speed, scale, and effectiveness of GTM operations and field execution
Design and deploy AI-native workflows that combine agents, automation, systems integrations, and human review
Build internal tooling and operational accelerators using OpenAI APIs, Codex, Salesforce, automation platforms, and lightweight applications
Rapidly prototype and iterate on solutions that improve forecasting, pipeline management, account planning, territory operations, deal execution, customer insights, and productivity
Partner directly with Sales, Solutions, RevOps, Systems, Finance, and Enablement teams to operationalize AI into day-to-day workflows
Build reusable “golden workflows,” templates, and operational tooling that can scale across the GTM organization
Translate operational pain points into practical AI-enabled systems and automation opportunities
Help define best practices, governance, and guardrails for deploying AI within GTM operational workflows
Drive adoption through hands-on training, pair building sessions, documentation, and direct collaboration with operators
Serve as a strategic thought partner to GTM leadership on how AI can reshape operational processes and field productivity

You might thrive in this role if you:

Have a strong systems and product mindset and enjoy turning ambiguous business problems into scalable solutions
Are excited about applying frontier AI capabilities to real operational and go-to-market workflows
Enjoy building practical systems quickly and iterating directly with users
Can deeply understand business processes while also thinking technically about architecture, integrations, and workflow design
Are comfortable operating across technical and non-technical stakeholders
Have experience with CRM systems, RevOps workflows, forecasting processes, analytics tooling, or GTM operations
Have experience building automations, internal tools, integrations, or AI workflows in production environments
Are highly analytical and comfortable using data to drive operational decisions
Thrive in ambiguity and can prioritize high-impact opportunities across multiple stakeholders
Have experimented with AI-native development tooling such as Codex, agents, workflow orchestration tools, or automation platforms

Experience

5+ years in Revenue Operations, Sales Operations, Business Systems, Workflow Engineering, Solutions Engineering, Product Operations, or related roles
Experience building internal tools, workflow automations, AI-powered systems, or operational infrastructure
Familiarity with APIs, automation frameworks, lightweight scripting, or AI workflow tooling
Experience working with GTM systems such as Salesforce, analytics tooling, forecasting systems, or workflow platforms
Strong analytical and operational problem-solving skills
Experience deploying systems used by operators in production environments
Ability to balance rapid experimentation with scalable operational design

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, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

OpenAI Global Applicant Privacy Policy

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Company Information

Company Name: Rockset

Company Website: https://rockset.com

Company Address: San Mateo, California, USA

Rockset is a cloud-native data platform company that builds and operates a managed, real-time analytics database designed to enable fast, low-latency SQL queries on continuously changing data streams and operational data stores. The company positions its core product as a purpose-built service for application developers, analytics engineers, and data teams who need to run complex queries—including joins, aggregations, and full-text search—against high-volume, high-velocity data without managing database infrastructure. Rockset’s offering emphasizes instant ingestion, automatic indexing, and serverless scalability so customers can get real-time insights from event streams, operational databases, and data lakes with minimal operational overhead. At the center of Rockset’s business activities is a managed database service delivered from the cloud. The service is designed to ingest data from a variety of sources—change data capture (CDC) streams, message queues, object storage, and direct connectors to operational databases—and make that data queryable immediately. Rockset automates the work that traditional analytics stacks require: it handles continuous ingestion, schema evolution, indexing, resource provisioning, and query optimization. The company provides a hosted, fully managed experience that abstracts the underlying infrastructure and offers predictable developer ergonomics and APIs. Rockset’s main product is the Rockset cloud database service, a serverless, managed analytics engine that supports ANSI SQL and enables low-latency queries on fresh data. Key product capabilities include continuous data ingestion from sources such as streaming platforms and change streams, automatic and adaptive indexing (often referred to as a “converged index” approach combining row, columnar, and inverted-index elements), a SQL query interface for expressive analytics and search, and a pay-for-what-you-use consumption model. Because of the indexing approach and the system’s ability to shard and parallelize queries across cloud infrastructure, Rockset aims to deliver millisecond-to-subsecond query latencies even on complex queries over large, rapidly changing datasets. Integration and extensibility are important parts of Rockset’s product strategy. The platform provides connectors and ingestion paths for common cloud and data stack components, enabling users to stream or batch data from sources such as message brokers, operational databases (via CDC), cloud object storage, and data warehouses. On the query and consumption side, Rockset supports standard interfaces that allow integration with applications and analytics tools: developers can query with SQL through REST APIs, client libraries, and standard connectors so that Rockset can be used as a back-end for dashboards, embedded analytics, personalization, recommendation engines, and search-oriented application features. Rockset also provides capabilities to join and enrich streaming or operational data with historical datasets, enabling hybrid analytical workloads that combine fresh and reference data. Operational characteristics of Rockset’s managed service emphasize elasticity and minimal operational burden. The platform is designed to be serverless from the user’s perspective—resources scale automatically to meet query and ingestion demand, and the user pays for compute and storage consumption rather than provisioning fixed clusters. Rockset also focuses on handling schema changes automatically as incoming data evolves, reducing the need for manual schema migrations that are common in traditional data warehouses. The managed nature of the service includes monitoring, automated failure recovery, and tuning handled by the platform, allowing engineering teams to focus on application logic and analytics rather than database administration. From a usage perspective, Rockset targets a set of real-time and near-real-time analytics use cases where freshness, performance, and developer productivity are critical. Typical applications include real-time dashboards and observability, customer 360 and personalization use cases, operational analytics, search-and-query experiences embedded in user-facing applications, and ad-hoc analytics on streaming event data. Rockset’s SQL-first approach helps lower the barrier to entry for analytics and data engineering teams already familiar with relational query language while also serving developers who need to embed queries in production applications. Security, governance, and enterprise readiness are presented as part of the managed offering: Rockset provides typical enterprise controls such as authentication and authorization, support for private network connectivity to cloud resources, and features intended to meet organizational security policies. The platform also exposes monitoring and auditing capabilities that help teams observe query performance and data flows. Rockset’s positioning is that customers can get the agility of real-time analytics while retaining operational safeguards required by corporate and regulatory environments. The company’s go-to-market focus combines direct product-led adoption—where developers can sign up and begin ingesting data and running queries quickly—with enterprise engagement for larger or production deployments that require integration, security reviews, and architectural guidance. Rockset’s documentation, SDKs, and examples emphasize rapid onboarding and practical recipes for building analytics and search features on top of streaming and operational data. Overall, Rockset is widely characterized in public materials as a cloud-native, real-time analytics database that abstracts infrastructure complexity and provides a SQL interface for immediate querying of continuously updated data. The product is aimed at teams that need real-time insights and application-facing analytics without the cost and complexity of managing specialized infrastructure or building custom indexing and ingestion pipelines.

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