Software Engineer, Codex for Teams
As a Software Engineer on the Codex for Teams team, you will be responsible for shaping the evolution of Codex by identifying how teams actually use and sometimes break AI-powered software engineering tools, driving changes across product, infrastructure, and model behavior to make Codex a reliable teammate for organizations. You will build core team and enterprise primitives that enable Codex to scale, including role-based access control (RBAC), admin and audit surfaces, usage and rate limits, pricing controls, managed configuration and constraints, and analytics for deep visibility into Codex usage. You will design and own secure, observable, full-stack systems that power Codex across web, IDEs, CLI, and CI/CD environments, integrating with enterprise identity and governance systems (SSO/SAML/OIDC, SCIM, policy enforcement) and developing data-access patterns that are performant, compliant, and trustworthy. The role involves leading real-world deployments and launches by working directly with customers and the Go To Market team to roll out Codex, using live usage and operational feedback to rapidly iterate and improve the product and platform capabilities. This position owns systems end-to-end, from architecture and implementation to production operations, emphasizing quality and velocity.
Solutions Engineer (AI/ML, Pre-Sales)
The Solutions Engineer (AI/ML, Pre-Sales) will work closely with strategic customers to understand their data curation needs, business challenges, and technical requirements. The role involves leading end-to-end customer proofs of concept (PoCs) that connect data curation to training behavior and evaluation outcomes, including dataset analysis, training plan design, and interpreting results. They will partner with customer machine learning teams to map data and curation strategies, design and execute evaluation plans for base and post-trained models, select appropriate benchmarks and metrics, and run model evaluations. Additionally, the engineer will produce customer-ready evaluation reports detailing methodology, metrics, baselines, ablations (e.g., curated vs raw data), conclusions, and recommendations for productionization. They must communicate technical results effectively to both ML experts and executive stakeholders, explaining tradeoffs in compute, latency, and deployment cost. Collaboration with go-to-market, engineering, and research teams is essential to deliver compelling demos, align on requirements, and incorporate customer insights into model training and product strategies. The role also includes providing technical guidance, training, and documentation to enable prospects to confidently assess the solution.
Machine Learning Engineer, Applied AI
The Machine Learning Engineer is responsible for leading applied AI initiatives by bridging research and product to turn generative models into production features across the first-party app and API. Responsibilities include experimenting rapidly, building rigorous evaluations and datasets, partnering with research, engineering, infrastructure, and product teams to ship reliable and scalable ML systems. They will fine-tune and deploy models for creative use cases such as text-to-image, image-to-text, image enhancement and editing, and multimodal applications. The engineer sets clear success metrics including quality, latency, and cost, and contributes to the safety, monitoring, and reliability of the systems. They lead projects from 0 to 1 that shape Applied AI practices at Ideogram while delivering features that bring value and delight to users.
Researcher, Synthetic RL
As a Research Scientist on the Synthetic RL team, you will develop novel reinforcement learning techniques that use synthetic environments and feedback to improve large-scale models. You will research and develop reinforcement learning algorithms, design and run experiments to study training dynamics and model behavior at scale, and collaborate with engineers and researchers to integrate successful approaches into model training pipelines.
Senior Software Engineer, Applied AI
As a Software Engineer working on AI systems, responsibilities include playing a foundational role in research, experimentation, and rapid improvement of AI systems to build a capable, reliable AI automation platform used worldwide in mission critical production environments. Tasks involve designing experiments and testing ideas to optimize key internal AI benchmarks, designing and improving evaluation frameworks to accelerate experimentation speed and direction, training, fine-tuning, and optimizing machine learning models, performing rigorous evaluation and testing for model accuracy, generalization, and performance, collaborating and contributing to core product development to enhance platform capabilities, and setting up observability and monitoring systems to safety check model behavior in critical settings.
Software Engineer - Frontend, Security Products
As a Full-Stack Software Engineer on the Security Products team, you will build, deploy, and maintain applications and systems that bring advanced AI-driven security capabilities to real users. You will work directly with internal and external customers to understand their workflows and translate them into intuitive, powerful product experiences. Your responsibilities include designing and building efficient and reusable frontend systems that support complex web applications, planning and deploying frontend infrastructure necessary for building, testing, and deploying products, collaborating across OpenAI’s product, research, engineering, and security organizations to maximize impact, and helping to shape the engineering culture, architecture, and processes of this new business unit.
Product Security Applied AI Intern, Summer 2026
Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.
Mechanical Engineer - Hands
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
Software Engineer, macOS Core Product - Palm Coast, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Hollywood, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and then design and implement solutions to address the highest priority issues.
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