Python AI Jobs

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Use proprietary software applications to provide input/labels on defined projects. Support and ensure the delivery of high-quality curated data. Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives/technologies. Interact with the technical staff to help improve the design of efficient annotation tools. Choose problems from economics fields that align with your expertise, focusing on areas like macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services that include labeling and annotating data in text, voice, and video formats to support AI model training. At times, record audio or video sessions as part of the role.

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Use proprietary software applications to provide input and labels on defined projects, support and ensure the delivery of high-quality curated data, play a pivotal role in supporting and contributing to the training of new tasks by working closely with technical staff to ensure the successful development and implementation of cutting-edge initiatives and technologies, interact with technical staff to help improve the design of efficient annotation tools, choose problems from economics fields aligned with expertise focusing on macroeconomics, microeconomics, and behavioral economics, and regularly interpret, analyze, and execute tasks based on given instructions. Additionally, provide labeling and annotating of data in text, voice, and video formats to support AI model training, sometimes involving recording audio or video sessions as required by the role.

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Translate research into product by working with client-side researchers on post-training, evaluations, safety, and alignment to build the necessary primitives, data, and tooling. Partner closely with core customers and frontier research labs to tackle technical challenges related to model improvement, performance, and deployment. Shape and propose model improvement work by translating customer and research objectives into technically rigorous proposals and execution plans. Lead the end-to-end lifecycle of projects including discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping solutions, and scaling successful pilots. Lead high-stakes engagements with senior stakeholders, define success metrics, identify risks, and drive programs to measurable outcomes. Collaborate across teams including research, platform, operations, security, and finance to deliver production-grade results. Design and implement robust evaluation frameworks, ensure data quality and feedback loops, and share learnings to elevate technical execution across accounts.

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Use proprietary software applications to provide input and labels on defined projects. Support and ensure the delivery of high-quality curated data. Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives and technologies. Interact with the technical staff to help improve the design of efficient annotation tools. Choose problems from economics fields that align with your expertise, focusing on areas like macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services that include labeling and annotating data in text, voice, and video formats to support AI model training, including recording audio or video sessions as required.

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As a Research Engineer in OpenAI's Monetization Group, you will design and deploy advanced machine learning models to solve real-world problems, bringing research from concept to implementation and creating AI-driven applications with direct impact. You will collaborate closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Your work includes implementing scalable data pipelines, optimizing models for performance and accuracy to ensure they are production-ready, and monitoring and maintaining deployed models to ensure they continue delivering value. You will stay ahead of developments in machine learning and AI by engaging with the latest research, participate in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

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Translate research into product by working with client-side researchers on post-training, evaluations, safety/alignment, and building necessary primitives, data, and tooling. Partner closely with core customers and frontier AI labs to address complex technical problems related to model improvement, performance, and deployment. Shape and propose model improvement work through clearly defined technical proposals, scope of work, and execution plans. Own the end-to-end lifecycle of projects, including discovery, writing product requirement documents (PRDs) and technical specifications, prioritizing trade-offs, conducting experiments, shipping solutions, and scaling pilots into repeatable offerings. Lead high-stakes technical engagements by running working sessions with senior stakeholders, defining success metrics, identifying risks early, and driving programs to measurable outcomes. Collaborate across multiple departments such as research, platform, operations, security, and finance to deliver reliable production-grade results. Build rigorous evaluation frameworks, maintain data quality feedback loops, and share insights to improve technical execution across accounts.

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The role involves translating frontier AI research into product by working with client-side researchers on post-training, evaluations, safety/alignment, and building the necessary primitives, data, and tooling. It requires partnering closely with core customers and frontier labs to solve complex technical problems related to model improvement, performance, and deployment. Responsibilities include shaping model improvement work by creating clear, technically rigorous proposals and execution plans, collaborating on production impact activities, leading the end-to-end lifecycle from discovery through to scaling successful pilots, and independently running high-stakes technical sessions with senior stakeholders. The position entails working cross-functionally with research, platform, operations, security, and finance teams to deliver reliable, production-grade solutions and contribute to building robust evaluation frameworks at the frontier that elevate technical execution across accounts.

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The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, building the necessary primitives, data, and tooling. The engineer partners closely with leading AI teams and frontier research labs to solve complex technical problems related to model improvement, performance, and deployment, shaping and proposing technically rigorous model improvement work. Responsibilities include leading the end-to-end lifecycle from discovery to scalable pilots, conducting technical working sessions with senior stakeholders, defining success metrics, managing risks, and driving programs to measurable outcomes. The role requires collaboration with research, platform, operations, security, and finance teams to deliver reliable, production-grade solutions. Additionally, the engineer designs and establishes robust evaluation frameworks, closes feedback loops on data quality, and shares best practices across accounts.

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Partner with internal stakeholders to understand their daily workflows to surface and document those that can be automated with AI. Build autonomous and semi-autonomous agent workflows that interact with browsers, codebases, and APIs to complete complex content operations. Learn and contribute to prompt engineering strategies, with guidance on building agentic workflows, memory systems, and multi-step reasoning. Build and operate MCP Servers that can be called by AI Agents. Design and build proprietary agent libraries, prompting strategies and benchmarking frameworks. Build and operationalize AI solutions with retrieval techniques (e.g., RAG, vector DBs) for context-aware applications. Prototype, test, and optimize AI-powered applications, including retrieval-augmented generation, workflow automation, and agentic experiences. Collaborate with cross-functional teams to align AI features with user needs and business goals, gaining exposure to product and platform thinking. Stay current on advancements in AI technologies, frameworks, and best practices, and evangelize AI capabilities internally and externally. Provide technical support, documentation, and training to facilitate adoption and effective use of AI solutions. Participate in technical discussions, architecture reviews, and sprint planning. Contribute to knowledge sharing and technical documentation.

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Lead a cross-functional team including engineers, designers, data scientists, and researchers to develop generative AI-enabled solutions for external riders and internal operations. Drive discovery into unmet needs, shape product vision, define priorities to achieve customer and business objectives, establish success metrics, and explore technical feasibility. Work closely with leadership across Product & Experience, Software, and Vehicle Engineering to implement AI solutions for the ride-hail service. Design AI-generated capabilities to enhance consumer experience, utilize data and market insights to guide product strategies, integrate user research into product requirements, oversee planning and management of tools and product scalability, collaborate with engineers and designers, coordinate cross-functional teams to meet milestones, lead the creation and launch of generative AI products, and develop and analyze performance metrics to gauge product success.