Staff / Senior Staff Backend Engineer, B2B Flywheel

OpenAI•San Francisco, CA

About The Position

B2B Flywheel is a small, high-leverage team working at the intersection of data science, research, and engineering within OpenAI’s B2B organization. The team is focused on building the systems that help OpenAI better understand how businesses and developers use our models, where they encounter friction, and how those signals can improve our products, models, and customer experiences over time. We operate in highly constrained production environments, including privacy safeguards, low-latency services, and high-traffic API systems. Our work spans real-time backend engineering, data infrastructure, research engineering, and applied data science. The team is small and highly execution-oriented, with broad ownership across the systems that capture, process, and translate critical product and usage signals into better B2B experiences. We’re looking for backend software engineers to help build the data and systems infrastructure behind OpenAI’s B2B flywheel. This role is ideal for someone who enjoys working across backend systems, data pipelines, and applied ML/DS-adjacent problems — especially in ambiguous, fast-moving environments where the path is not always predefined. You’ll help design and build real-time engineering systems that operate across high-scale API and B2B surfaces, while partnering closely with data science, research, product, and engineering teams. Strong candidates will bring deep backend engineering ability, comfort with data-heavy systems, and the versatility to work across multiple technical domains.

Requirements

7+ years of professional backend engineering experience, ideally in high-scale, product-driven, or infrastructure-heavy environments
Strong backend engineering fundamentals, including distributed systems, API services, data modeling, reliability, performance, and production operations
Experience building or maintaining data pipelines, event systems, logging/telemetry infrastructure, or large-scale data processing systems
Comfort working near data science, statistics, ML, or research engineering workflows; direct experience is strongly preferred, but not required
Ability to operate effectively in ambiguous environments where problems are loosely defined and ownership boundaries are broad
High agency, strong execution velocity, and a willingness to wear multiple hats across backend engineering, data infrastructure, and applied research-adjacent work

Nice To Haves

Experience in fast-paced startups, early teams, or similarly high-ownership environments is a plus
Interest in AI systems, enterprise products, and building the infrastructure that helps improve products and models through real-world usage signals

Responsibilities

Design, build, and operate backend systems that capture and process critical product, usage, and customer signals across B2B and API surfaces
Build reliable data pipelines and infrastructure that help translate raw signals into actionable insights, model evaluations, training datasets, and product improvements
Work on high-traffic, low-latency services in constrained environments, including systems with strict privacy and data-handling requirements
Collaborate closely with data science, research, product, and infrastructure partners to identify user pain points, measure system behavior, and improve product quality
Help develop the system needed to support model and product feedback loops, including signal capture, evaluation datasets, classifiers, and analytical tooling
Operate with high ownership in a small team where priorities may shift quickly and the work spans multiple engineering domains