Software Engineer, Implicit Signals

About the Team Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping researchers assess changes and improve models and products. We work closely with researchers and data scientists and have broad ownership of the software that makes these capabilities available. Our work spans real-time systems, data engineering, model serving, and tools for experimentation, with careful attention to performance, reliability, and privacy. About the Role We’re looking for a software engineer to build and evolve our classification and evaluation platform. You’ll develop services, online and offline pipelines, and tools that help researchers and data scientists turn an idea into a working measurement and iterate on it quickly. This role combines software and data engineering with hands-on production ownership. You’ll shape the architecture, deliver new capabilities, improve inference efficiency, and keep the system dependable as workloads and requirements evolve. Strong candidates will bring deep software engineering expertise and an interest in building a technical product in close partnership with its users. In this role, you will: - Design and build software services and APIs for configuring, running, and managing classifiers and evaluations. - Build and operate online and offline classification pipelines that support reliable, efficient execution at scale. - Improve model-serving performance, including throughput, latency, concurrency, and compute utilization. - Build tools that make experimentation, debugging, and iteration straightforward for researchers and data scientists. - Own production reliability, observability, and incident response, and reduce the operational burden of running the platform. - Partner with machine learning engineers to bring new classification capabilities into production. - Work closely with post-training researchers and data scientists to understand their workflows, identify high-impact improvements, and shape the product. You might thrive in this role if you have: - Experience designing, building, and operating software services or distributed systems at scale. - Strong fundamentals in API design, concurrency, caching, performance, and failure recovery. - Ability to investigate complex production problems and make sound architectural tradeoffs. - A track record of owning software from initial design through deployment and ongoing operation. - Strong product judgment and an interest in working directly with technical users. - Comfort with ambiguous problems and broad ownership across multiple parts of a system. - Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment. - Interest in machine learning, data science, and statistics; experience with model serving, ML platforms, or inference optimization is a plus. 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 se

Experience designing, building, and operating software services or distributed systems at scale. Strong fundamentals in API design, concurrency, caching, performance, and failure recovery. Ability to investigate complex production problems and make sound architectural tradeoffs. A track record of owning software from initial design through deployment and ongoing operation. Strong product judgment and an interest in working directly with technical users. Comfort with ambiguous problems and broad ownership across multiple parts of a system. Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment. Interest in machine learning, data science, and statistics; experience with model serving, ML platforms, or inference optimization is a plus.

Design and build software services and APIs for configuring, running, and managing classifiers and evaluations. Build and operate online and offline classification pipelines that support reliable, efficient execution at scale. Improve model-serving performance, including throughput, latency, concurrency, and compute utilization. Build tools that make experimentation, debugging, and iteration straightforward for researchers and data scientists. Own production reliability, observability, and incident response, and reduce the operational burden of running the platform. Partner with machine learning engineers to bring new classification capabilities into production. Work closely with post-training researchers and data scientists to understand their workflows, identify high-impact improvements, and shape the product.