Engineering Jobs

Engineer, Product Engineering (Media Health Reliability), Heteregenous Integration - High Bandwidth Memory — Fab 10A, Singapore. Apply via Workday.

MTS, AI Engineering, SMAI — 2 Locations. Apply via Workday.

Senior Engineer, Product Engineering (Media Health Reliability), Heteregenous Integration - High Bandwidth Memory — Fab 10A, Singapore. Apply via Workday.

Principal Engineer, Electrical & Control Engineering — Fab 10N/X, Singapore. Apply via Workday.

Senior/Staff Engineer, Product Engineering, Media Health Manufacturing, HIG-HBM — Fab 10A, Singapore. Apply via Workday.

Razorpay is one of India’s leading full-stack financial technology companies, powering the way businesses move, manage, and grow money. Founded in 2014 by Harshil Mathur and Shashank Kumar with a simple vision - to simplify payments for Indian businesses - we’ve since grown into a fintech powerhouse driving India’s digital payment revolution. Razorpay powers millions of businesses with a smarter, scalable stack that goes beyond transactions to help them truly build and grow. From building AI-native agentic payments, to AI-assisted fraud detection and real-time risk intelligence to automated reconciliation, smart payouts, and predictive financial insights, we are embedding intelligence across our stack to make money movement faster, safer, and more efficient. In close collaboration with ecosystem partners - including banks, networks, regulators - we are pioneering industry-first solutions that are shaping the next era of fintech Across India, Singapore and Malaysia, our products span everything from seamless checkouts to payroll automation - powering a fintech ecosystem that’s redefining how money moves across Asia. Today, that ecosystem supports everyone from early-stage startups to some of India’s largest enterprises, enabling them to accept, process, and disburse payments at scale while expanding into new ways of managing money more efficiently. Our scale speaks volumes: Razorpay processes $180+ billion in annualized transactions, powering leading businesses like Airbnb, Facebook, WhatsApp, Airtel, CRED, BookmyShow, Zomato, Swiggy, Lenskart, Mirae Asset Capital markets, Indian Oil, National Pension Scheme - and over 100 of India’s unicorns. With strong roots in India and growing operations in Southeast Asia, we are shaping the next chapter of financial technology across the region. We are backed by global investors including GIC, Peak XV Partners (formerly Sequoia Capital India & SEA), Tiger Global, Ribbit Capital, Matrix Partners, MasterCard, and Salesforce V

About the Team OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. The Engineering Acceleration team builds products that multiply the effectiveness of OpenAI’s technical teams, helping engineers, researchers, and product teams understand complex systems, learn from what they ship, and operate reliably at scale. As AI changes how software is built, we have an opportunity to rethink engineering workflows from first principles. We’re creating tools and shared systems that turn complex data, experimentation, and technical workflows into clear decisions and useful action. About the Role In this role, you’ll lead design across two connected product areas: a real-time data exploration and observability experience for investigating large-scale system and product behavior, and an experimentation platform for safely launching changes, measuring their impact, and deciding whether to ramp, iterate, or roll back. This is more than a dashboard-design role. You’ll define the interaction models that take someone from a vague question or unexpected signal to a trustworthy answer and clear next step. You’ll work closely with engineers, researchers, data scientists, and product teams to understand the mechanics of their work and make dense technical systems coherent without flattening the details that matter. You’ll also help establish greater consistency across OpenAI’s enterprise and internal tools, developing durable patterns that support AI-native workflows and enable other designers to build more effectively. This role is based in our Seattle, WA or San Francisco, CA offices. We offer relocation assistance to new employees. In this role, you will: Lead end-to-end design for data-intensive products used by engineers, researchers, and product teams. Shape the complete learning loop: instrument, launch, observe, investigate, evaluate, decide, and iterate. Create clear, high-craft workflows for querying, filtering, comparison, drill-d

The Cyber Deployment Manager partners with customers throughout the full lifecycle—from technical discovery and solution design through implementation, deployment, and adoption. You’ll work closely with Sales and Solutions Engineering during the pre-sales process to understand customer security priorities, assess technical requirements, develop solution architectures, and support demonstrations, workshops, and proofs of concept. After a customer commits, you’ll remain engaged as the technical deployment lead, translating the proposed solution into a production-ready implementation. You’ll guide integrations, establish success criteria, manage technical risks, and help customers operationalize AI across security workflows such as secure code review, vulnerability management, threat detection, incident response, SOC operations, and GRC automation. In this role, you will: Lead technical discovery with security executives, practitioners, architects, and engineering teams. Partner with Sales and Solutions Engineering on solution design, demonstrations, workshops, technical validation, and proofs of concept. Translate customer requirements into clear architectures, deployment plans, success criteria, and implementation milestones. Own the transition from pre-sales solution design into post-sales deployment and adoption. Serve as the primary technical partner during implementation, coordinating customer stakeholders and internal Product, Engineering, Security, and GTM teams. Build and troubleshoot integrations involving APIs, agents, security tools, cloud platforms, data sources, and enterprise workflows. Identify deployment risks, technical blockers, and product gaps, and drive them toward resolution. Help customers establish evaluation frameworks, governance controls, guardrails, monitoring, and human-review processes. Measure adoption and business impact, ensuring deployed solutions deliver meaningful security outcomes. Turn successful customer deployments into reusable

About the Team OpenAI’s Governance, Risk, and Compliance team helps ensure security and privacy are grounded in how our products and systems actually operate. Assurance Operations partners with Security, Engineering, Infrastructure, Product, Privacy, and Legal to make controls provable, risk decisions explicit, and audit readiness a result of well-designed systems. About the Role We are hiring a technical, product-minded GRC builder who can own consequential audits while improving the control and evidence systems behind them. You will build a reusable common control framework, use Codex to automate assurance work, validate changing system scope, and turn repeated audit friction into measurable improvements. We are looking for someone who questions inherited assumptions, solves novel problems creatively, works closely with engineers, and makes the next audit easier by improving the underlying system. You’ll be responsible for: Lead external, internal, customer, and certification audit work from scoping through evidence review, fieldwork, remediation, and closeout. Build a common control framework linking risk, control intent, implementation, owner, system, environment, evidence, and applicable frameworks. Validate actual scope and ownership instead of assuming last year's controls, product boundaries, or evidence remain accurate. Use Codex to build and test evidence checks, control mappings, request triage, owner workflows, monitoring, and remediation reporting. Partner with engineers on cloud architecture, identity, logging, data flows, software changes, vulnerabilities, and control effectiveness. Design maintainable, permission-aware tools that preserve source provenance, human review, and evidence integrity. Reduce repeated requests and operational burden for control owners through measurable workflow improvements. Define roadmaps, decision rights, milestones, success metrics, and clear cross-functional escalations. We’re looking for someone with: Direct ownership

About the Team The Applied AI team is responsible for ensuring the safe and effective deployment of Generative AI applications for developers and enterprises. We act as a trusted advisor and thought partner for our customers, working to build an effective backlog of GenAI use cases for their industry and drive them to production through strong technical guidance. As the leader of our ADEs in the Large Enterprise segment, you’ll help companies transform their business through solutions such as customer service, automated content generation, and novel applications that make use of our newest, most exciting models. About the Role We are seeking an Applied AI Engineering leader to ensure the technical success of our most strategic Large Enterprise customers across EMEA. In this role, you will manage the entire implementation journey, ensuring seamless platform integration. As the voice of our customers, you will align technical teams to deliver a consistent and exceptional experience throughout the customer lifecycle. Success will be measured by live production applications, increased API adoption, and impactful customer stories that demonstrate the value of our technology. This role is open in both our London and Munich offices. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own the strategy and operating model of the Large Enterprise Applied AI team, ensuring alignment with company objectives and the evolving needs of our customers. Lead, build, and mentor a team of high-performing ADEs to deliver exceptional customer outcomes, as demonstrated by production customer applications and increased API adoption. Serve as the technical advocate for our customers, synthesizing their needs to develop the Research and Applied Product/Engineering roadmaps. Act as the primary technical escalation point during development, fostering trust and maintaining direct communication with executive-level

About the company OpenAI’s mission is to build and ensure that safe artificial general intelligence (AGI) benefits all of humanity. This long-term undertaking brings together the world’s best scientists, engineers, and business professionals to accomplish this. About the role We are looking for a Sales Engineering leader to be responsible for overseeing all technical aspects of the OpenAI sales process ensuring developers and enterprises maximize benefit, value, and adoption from our highly-capable models and technology. We are looking for a technical leader who represents the voice of the customer, aligning our customer-facing technical teams around a unified and exceptional experience across the customer lifecycle. You will be crucial to the success of our GTM strategy and will have a significant influence in shaping the direction of our technology and those who adopt it. Your responsibilities will encompass overseeing all technical aspects of the sales process for our Digital Natives segment. This includes not only the pre-sales activities but also at times the post-sales technical support ensuring a cohesive and outstanding experience for our clients. Your teams of Solutions Engineers will rely on your leadership to guide them in delivering exceptional service and technical expertise. You will work closely with Sales, Customer Engagement, Security, and Product teams. In this role, you will: Help craft and continuously refine the strategic vision for OpenAI's Solutions Engineering function, aligning it with broader company objectives and the evolving needs of our diverse clientele. Lead and mentor a multifaceted team through the entire sales cycle, fostering an environment that promotes seamless customer engagement, maximum adoption, and scalability. Collaborate with cross-functional teams including GTM, Product, Engineering, and Research, ensuring synergy and cohesion in our customer engagement strategies. Act as the escalation technical contact for key clients

About the Team The Applied team brings OpenAI’s technology to the world through products used by hundreds of millions of people and by developers and businesses building on our APIs. We work across research, engineering, product, policy, safety, and operations to deploy frontier AI systems responsibly and safely. The Trust & Safety Data Engineering team builds the data foundations that help OpenAI understand, detect, investigate, and mitigate abuse and safety risks across our products. We partner with Integrity, Investigations, Safety Systems, Product Policy, Privacy, Data Science, Engineering, and Data Platform to create reliable, privacy-safe datasets and pipelines for fraud and abuse detection, enforcement workflows, safety measurement, ML feature generation, launch readiness, and transparency reporting. About the Role We are hiring a Technical Lead Manager to lead and grow the Trust & Safety Data Engineering team. This is a hands-on leadership role for someone who can set strategy, shape data architecture, align senior stakeholders, coach engineers, and drive execution on high-impact data systems. You will help turn fragmented launch and incident support into durable, reusable, privacy-safe data foundations that Trust & Safety teams can rely on. The systems your team builds will help OpenAI detect risk, investigate abuse, power operational workflows, develop and evaluate safety models, measure interventions, support product launches, and report accurately on platform integrity. In This Role, You Will Lead and grow a high-performing Trust & Safety Data Engineering team. Define the roadmap and technical strategy for Trust & Safety data systems. Build canonical, privacy-safe datasets and pipelines for abuse detection, fraud detection, risk signals, enforcement, scaled review, transparency reporting, and safety monitoring. Create reusable foundations for Trust & Safety model development, including features, labels, training data, backtesting,

About the team The Applied team at OpenAI safely brings cutting-edge technology to the world. We have released groundbreaking products such as ChatGPT, Plugins, DALL·E, and APIs for GPT-4, GPT-3, embeddings, and fine-tuning. Our team also manages large-scale inference infrastructure. With much more on the horizon, our impact continues to grow. Our customers create fast-growing businesses using our APIs, enabling product features previously unimaginable. ChatGPT exemplifies the current scope of possibilities. We prioritize the responsible use of our powerful tools, valuing safe deployment over unchecked expansion. Within Applied Engineering, the Financial Engineering team ensures that our products are monetized effectively to accommodate customers' varying needs and scales. Collaborating closely with the GTM and Finance teams, we strive to tailor our billing stack to our evolving internal requirements. We seek an experienced engineer to architect and refine our billing systems, enhancing their functionality to meet the demands of our increasingly complex and expansive product offerings. In this role, you will: Architect and build the next generation of billing and monetization systems at OpenAI. Develop across the stack to create comprehensive billing integrations for our range of ChatGPT and API users. Design a versatile billing platform suitable for both subscription and usage-based offerings, ensuring scalability and enterprise readiness/flexibility. Construct and integrate tools that empower internal teams to seamlessly incorporate billing data into their workflows. Collaborate closely with a wide array of stakeholders, including the Product, Data, Finance, and Go-To-Market teams, as well as fellow engineers. You might thrive in this role if you: Possess a minimum of 5 years of professional software engineering experience, with added experience in payments, billing, or monetization seen as a bonus. Enjoy engaging with various partners, particularly those outside

About the Team Our team turns OpenAI’s latest model capabilities into polished, trusted products for consumers and developers. We build the end-to-end experiences including product surfaces, platform layers, and developer workflows that make cutting-edge AI accessible, useful, and dependable at scale. OpenAI’s Financial Engineering (FinEng) team powers how revenue flows through our products - pricing and packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner closely with Engineering, Data Science, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide. We pair rapid innovation with a rigorous approach to responsible deployment. Safety and trust are built into how we design, ship, and learn from real-world usage, so these tools deliver meaningful value while aligning with OpenAI’s mission. About the Role We are seeking an experienced Product Manager to scale the product efforts and technical strategy within our Financial Engineering team. The ideal candidate has prior experience in billing, finance, and accounting, ideally also building solutions for commercial users of varying sizes from small scale to enterprise. This role requires close collaboration with our product, finance, operations, and engineering teams. This position is based in San Francisco, CA. We utilize a hybrid work model with 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Develop a strategy and roadmap to efficiently scale the billing operations and customer experience behind OpenAI’s growing product portfolio Identify and execute opportunities to improve the order-to-cash processing for OpenAI’s largest and most strategic customers Build AI powered tooling for key partner teams such as Finance and User Operations to drive better decisions and business outcomes Collaborate with other product teams to defining OpenAI’s evolving monetization s

About the Team The Safety Systems org is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Safety Engineering team builds the platforms and tools that make OpenAI’s models safe to use in the real world. We partner closely with researchers, product teams, and policy to turn safety ideas into reliable, scalable systems: measuring risk, enforcing safeguards, and continuously improving how models behave in production. Our work sits at the intersection of product engineering, data, and AI, and directly shapes how millions of people experience OpenAI’s technology. About the Role We’re looking for a self-starter engineer who loves building products in an iterative, fast-moving environment—especially internal tools that unlock real-world impact. In this role, you’ll build full-stack tooling for our Safety Systems teams that directly improves the safety and reliability of OpenAI’s models, including in sensitive areas like mental health and other vulnerable-user protections. Your work will increase the team’s velocity in identifying and fixing safety issues and help tighten the feedback loop between policy, data, and the model training cycle. In this role, you will: Own the end-to-end development of internal tools that help improve the safety of OpenAI’s models (with a focus on areas like mental health and other vulnerable-user protections) Partner closely with Safety Systems researchers, engineers, and model policy creators to understand workflows, pain points, and requirements—and translate them into durable product solutions Build full-stack experiences to support core model policy workflows, such as labeling and inspecting data, analyzing and reviewing failure cases, and surfacing insights for iteration Optimize internal applications f