Software Engineer, Backend
As a backend engineer, you would play a critical role in the search architecture at Exa. Your work may involve building massive-scale machine learning systems, working on projects based on your skills and interests, such as recreating Google-level keyword search over 10 billion pages in one month, building state-of-the-art crawling systems that work optimally for any website, and building custom vector databases that can run over a billion vectors in under 100 milliseconds.
AI Deployment Engineering Manager, Startups
The AI Deployment Engineering Manager, Startups, is responsible for leading and scaling the Startups AI Deployment Engineering team to help high-growth startups move quickly from experimentation to production, unlock meaningful usage, and build durable technical partnerships with OpenAI. This includes crafting and continuously refining the strategic vision and operating model for the team to align with OpenAI's broader objectives and startup customer needs, leading and mentoring a team of technical individual contributors, identifying technical blockers for startups and advising on architecture and deployment paths, partnering closely with Sales to accelerate adoption and account growth, representing the technical voice of startup customers by synthesizing feedback, translating recurring startup needs into repeatable playbooks and assets, serving as a senior technical escalation point for priority startup customers, balancing urgent customer needs with OpenAI's product and platform priorities, coaching AI Deployment Engineers on various skills including technical quality and executive communication, and collaborating across multiple OpenAI departments to enhance support for startups from early adoption through scaled production usage.
Solutions Architect (APAC)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They also design multi-agent systems using different patterns, implement agent logic with modern frameworks (langchain/langgraph), create evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. Additionally, they lead technical maturity assessments, work directly with enterprise customers to understand requirements and offer recommendations, and collaborate with Engagement Managers and Product/Engineering teams.
Machine Learning Engineer (Singapore)
Build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. Own the full pipeline from raw content to curated, filtered, and training-ready datasets focusing on speed, reliability, reproducibility, and cost-efficiency. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Design and implement curation pipelines for selection, filtering, and retention of video and image content for model training including image-text pair datasets. Build and improve VLM-based captioning and metadata generation workflows at scale across video and image data. Develop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction approaches for data selection. Build tooling to support deduplication workflows at scale, including near-dedup and exact deduplication pipelines over large video corpora. Analyze dataset composition, identify quality issues, iterate on curation logic to improve training outcomes. Define and evolve standards for high-quality, training-ready video data across different training regimes.
Research Scientist (Singapore)
Drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora. Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, focusing on preserving or improving generation quality while reducing inference cost. Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across aesthetics, motion quality, and prompt adherence. Analyze the relationship between base model behavior and post-training outcomes, working with foundation model team to inform pretraining decisions accordingly.
AI Deployment Engineer, Codex | Singapore
The AI Deployment Engineer will serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with their engineering teams to build coding workflows. They will partner directly with customers to design and implement AI-enhanced development workflows from rapid prototyping through scalable production rollout. Responsibilities also include building high-quality demos, reference implementations, and workflow automations using Codex as part of the development process. The engineer will lead large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. They will contribute technical content such as examples, guides, patterns, and best practices to the OpenAI Cookbook to assist the broader developer community. Additionally, the role involves gathering high-fidelity product insights from customer deployments and translating them into product proposals and model feedback for internal teams. The engineer will influence customer strategy and decision-making regarding the integration of AI coding tools into their software development lifecycle, technical roadmap, and workflows. Finally, they will act as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Software Engineer, Simulation
As a Robotics Application Engineer specializing in Intelligent Manufacturing Automation, the responsibilities include closely collaborating with the automation group and industry partners to advance manufacturing automation by extending capabilities and solutions. Daily tasks involve integrating and developing robotics solutions, including processes, software features, and integrating state-of-the-art AI for manufacturing automation. The role includes working with the team leading the deployment of impactful robotic systems in production. Additional responsibilities entail contributing to the technical development and integration of advanced robotic automation solutions for manufacturing automation using the Intrinsic platform, ROS, and AI capabilities, collaborating with research and industry partners to integrate AI and automation into factory settings, and documenting designs, processes, and results while communicating effectively with internal technical teams and partners.
Enterprise Account Executive
The role involves defining the internal AI roadmap by partnering with Security, Legal, and business leaders, operating the enterprise AI stack including LLMs, vector databases, and gateways, enforcing consistent patterns for tool calling, prompt versioning, state management, and error handling to prevent fragmented agent implementations, and managing the full model lifecycle from evaluation and testing to upgrades and deprecations. The position includes proactively interviewing teams like Talent, Support, and Sales to identify manual workflows for automation with agentic AI, independently building and deploying proof of concepts to demonstrate ROI before scaling, owning the token procurement process, building forecasting and chargeback models to control spend, creating dashboards to track SLAs and system performance, and identifying opportunities for cost-saving and performance tuning.
Forward Deployed Engineer - Singapore
Forward Deployed Engineers lead complex end-to-end deployments of frontier models in production alongside strategic customers, owning discovery, technical scoping, system design, build, and production rollout. They operate across multiple deployments from prototype to stable production, build full-stack systems to deliver customer value, embed closely with customer teams to understand needs, guide adoption, scope work, sequence delivery, remove blockers, make trade-offs between scope, speed, and quality, contribute directly in code when necessary, codify working patterns into reusable tools and playbooks, share field feedback to help Research and Product teams improve models, and keep teams moving with clarity and follow-through.
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