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Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. Your Location: This position is CHINA BASED . The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. Your recruiter will inform you what cities you are able to work from depending on your personal legal working identity and Airbnb internal policies. The Community You Will Join: Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. The Community Support Products (CSP) Machine Learning team is the core team responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. The richness of Airbnb's data, the complexity of its marketplace and the variety innate in our product mean that we need to operate at the state of the art of AI practice. We are committed to investing in long term innovation to solve the complex problems we face, and to do that we need the very best experts in ML and AI to join us. The Difference You Will Make: We believe our current customer experiences in these domains are only scratching the surface of the innovations that are possible

About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Risk Detection is focused on providing a delightful experience for our merchants and minimizing friction, while ensuring the safety of our users in the financial ecosystem. Whenever the Risk team takes action on an account, we work to notify Merchants and provide clear status on what’s happening, enable guided workflows for resolving their issues, and redefine our overall Risk processes to make them as smooth as possible for good merchants. What you’ll do We’re looking for an engineering leader to lead and grow a strong team of engineers, build relationships with customers internally and externally, and champion our vision of making Stripe’s risk management a feature that attracts and retains merchants, and becomes a product differentiator. This is an exciting opportunity to partner with teams across Stripe to build the best merchant experience, and contribute directly to Stripe’s growth. Responsibilities Support the team in delivering a high level of technical quality and impact via APIs, user-facing experiences, services, and systems Recruit, hire, scale, and develop an amazing team of engineers Executing cross-functionally with leadership, product teams, infra teams & risk strategists Be actively involved in strategic direction and platform decisions that impact all of Stripe and Stripe customers Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you

Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. Design systems to speed up the time from idea to deployment of new models. Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. Develop pipelines and automated processes to train and evaluate models in offline and online environments. Integrate ML models into production systems and ensure their scalability and reliability. Collaborate with product and strategy partners to propose, prioritize, and implement new product features. Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production. Must have two (2) years of experience in each of the following: ML algorithms and model architectures; Designing, training and evaluating machine learning models; Productionizing and deploying machine learning models at scale; Orchestrating data pipelines and leveraging large-s

We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.) Our Mission: To actively connect people to their next great opportunity. Who We Are: ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company actively connects millions of all-sized businesses and job seekers through innovative mobile, web, and email services, as well as through partnerships with the best job boards on the web. ZipRecruiter has the #1 rated job search app on iOS & Android. About the Team: Jobs change lives - ask any one of our millions of job seekers we serve and you’ll no doubt get a similar answer. At ZipRecruiter, our products help connect job seekers with employers, eager to add talent to their company. Our team works on applications at scale, serving millions of job seekers. We’re building an innovative marketplace for both jobs and job seekers alike. As part of this effort, we’re hiring an Engineering Manager to lead our Machine Learning Engineering team. Our business is complex and we’re looking for engineering managers who can own entire (or even multiple) product lines, drive business value while investing in their team’s/tech stack’s health, and coach engineers to build a strong culture of excellence, ownership, and personal and professional growth. About the Job: Own the mission, execution, morale, and code quality of your team Partner effectively with product managers to maintain and execute against the product roadmap Serve as a force multiplier for your team by removing roadblocks, identifying and implementing process improvements, and delegating effectively Actively build community and culture throughout the engineering organization Continually develop, coach, motivate, and improve your team, leading by example Champion and evolve innovation at ZipRecruiter and evangelize BIG ideas Minimum Q

About the Team OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS . The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied. The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including AWS-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure. About the Role We’re hiring Machine Learning Engineers to build and improve the AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments. This role spans post-training workflows, evaluation, data pipelines, model behavior, and API/infrastructure integration. You’ll work at the boundary between partner needs and core ML systems: helping teams understand what is and isn’t working, diagnosing issues in training and evaluation workflows, and turning those learnings into improvements to the underlying platform. You should enjoy working with external technical partners, extracting the real goal from messy requests, and pushing back or reframing when the requested experiment is not the highest-leverage path. You’ll collaborate closely with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to solve ambiguous model-performance problems. When you succeed, strategic partners and internal teams will be able to improve model behavior with confidence, driving measurable product improvements while the systems behind that work become more reliable, scalable, and effective over time. In this role, you will Partner with strategic customers and in

About Druva Druva is the resilience foundation for the AI enterprise, helping organizations secure and recover from connected risk across data, cyber, identity, and AI. The Resilience Cloud is a fully managed, cloud-native SaaS platform that delivers air-gapped and immutable protection across cloud, SaaS, on-premises, endpoint, and edge environments. Powered by Dru MetaGraph, Druva’s graph-powered intelligence layer, the platform connects critical business context so customers can understand risk, respond faster, recover cleanly, and govern data with greater confidence. Trusted by nearly 7,500 customers, including 75 of the Fortune 500, Druva helps safeguard the critical information and systems businesses depend on in an increasingly connected world. Visit druva.com and follow us on LinkedIn , X and Facebook . About the Team The Learning & Development (L&D) team is dedicated to fostering a culture of continuous improvement and growth. The team provides essential resources for continuous skill development, enabling employee success. Our programs are designed to support high-ownership work, ensuring employees can thrive, deliver maximum ROI for customers, and build key career skills as Druva shapes the future of data security. Role Purpose & Impact The Senior Learning and Development Manager will architect and execute our enterprise-wide learning strategy, building scalable programs that drive leadership excellence, accelerate capability development, and strengthen employee engagement. This role reports to the Sr. Director, HR and serves as a strategic partner to senior leadership, translating business objectives into targeted development initiatives that enhance organizational performance and prepare our workforce for future challenges in the rapidly evolving data security landscape. What You’ll Do Strategic Learning Architecture Design and implement a comprehensive learning ecosystem aligned with business priorities, i

About the Team The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-To-Market teams to inform high-stakes decisions through rigorous data science and economic modeling. As part of our expanding Data Science function, we’re building a best-in-class Forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more. We are developing scalable forecasting infrastructure to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy - supporting key investment decisions and unlocking OpenAI’s full potential. About the Role We’re looking for a senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. Your models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, LTV, compute consumption, and profitability. This is a highly cross-functional role, requiring technical excellence, strong product intuition, and business acumen. You’ll collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI. This role is based in San Francisco, CA. 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: Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM domains. Own the end-to-end modeling lifecycle , including scoping, feature engineerin

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . We're looking for an experienced Senior Engineering Manager to lead our Media Ranking teams. In this role, you’ll lead a high-impact team of Machine Learning Engineers dedicated to improving relevance, personalization, and discovery of the core product. Reddit is investing big on making media (video and audio) as the core components of our experience around which conversations happen and users connect with the community. Your work will directly shape the personalized experiences of over 120M+ daily users. Responsibilities: Define Technical Vision & Strategy: Define the technical vision and long-term roadmap for Feed Relevance, aligning cutting-edge ML solutions with Reddit’s product and business objectives. Team Leadership & Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact. Cross-Functional Partnership: Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work. Technical Execution & Delivery: Oversee the design, development, and optimization of sophisticated relevance, discovery, and content distribution systems. Platform & Infrastructure Collaboration: Collaborate with platform and infrastructure teams to chart a path to the next generation of AI-powered solutions for Reddit. Operational Excellence: Maintain high standards for system performance, reliability, and efficiency in alignment with customer needs and responsible AI princip

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems. Responsibilities Push search quality forward through models, data, evaluation, infrastructure, or any other leverage available. Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely. Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate. Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring. Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity. Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome. Qualifications Deep understanding of search or recommender systems and their evaluation. Proven ownership of a large-scale production ranking system or a substantial class of quality problems. Strong machine learning and software engineering skills across data, models, serving, and monitoring. Ability to drive ambiguous, cross-team problems without continuous task decomposition. Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . As a Senior Staff Machine Learning Engineer, Feed Relevance , you'll drive the development of GenAI-powered recommendations, leveraging Reddit’s unique community landscape, rich conversational data, and dynamic engagement patterns. Your deep modeling expertise will directly shape the core architectures behind feed ranking, enabling breakthrough personalization for tens of millions of users. Responsibilities: Deliver on technical initiatives that have significant company-wide impact Set technical direction for the broader Relevance and Feeds teams at Reddit, able to identify opportunities and influence strategy across multiple orgs Work with management on goal setting, planning, and de-risking critical projects Mentor and grow Senior and Staff engineers Create a strong healthy engineering culture Qualifications: 10+ years of industry experience building systems for relevance driven products Subject matter expert in relevance, recommendation, and ML systems; able to solve complex problems in these domains that few others can Deep understanding of how to build sustainable software systems at a large scale engineering organization Experience in influencing organizations on technical direction/best practices Experience working with cross-functional teams such as design, product, business & data teams to deliver great experiences. Strong focus on user experience, usability, scalability, reliability and quality. You are an undying advocate for the user, and you have a deep intuition for how people & machines inte

About Prodigal Prodigal is the connected AI platform leading financial institutions use to run their operations. We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time. These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved? Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them. Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America. Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in one interaction can inform what happens next. We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect. We are backed by Y Combinator, Accel, and Menlo Ventures, and work with 100+ financial institutions across North America. Listen directly from our CTO, Cofounder - Sangram Raje About the DSML Team The Data Science and Machine Learning (DSML) team owns the full lifecycle of Prodigal's ML systems, from data curation to model selection, fine-tuning, and post-training, through to deployment, evaluation, and ongoing monitoring. We sit close to real product and customer problems, and we're expected to move from experiment

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evalua

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning fra

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. Agentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, and product engineers alongside data scientists and operations partners to transform these systems. As a Machine Learning Engineer on the SCC team, you will fine-tune and align models and build AI Agents that power how riders and drivers get help. Your work spans the full loop: post-training open-source models for our domain, composing them into multi-step agents, and building the evaluation that proves they are safe to ship in a customer-facing, safety-critical setting. Post-train and adapt open-source LLMs for SCC use cases using SFT, LoRA, and preference-tuning methods (RLHF, RLAIF, RLVR). Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks. Own the evaluation data flywheel, offline and online, that defines what "good" looks like and build benchmarks for the team to hill-climb. Turn interaction feedback into training data and learning signals, closing the data flywheel that continuously improves the models. Responsibilities: Conduct literature review and build post-training fra

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . We're looking for a highly technical Senior Engineering Manager with a deep understanding of modern recommendation systems to manage, lead and develop a team of ML researchers and engineers within the Applied Science team. In this role, you will lead the strategy of the Recommendation System innovation team. You will help the team build a balanced portfolio of work that addresses both immediate short-term business impact and long-term strategic breakthroughs in areas like Generative Recommendation System (GenRecSys), Large Sequential Models, and Reinforcement Learning (RL). Partnering closely with executive leadership, product management, and engineering partners, you will evolve our technical roadmap and directly drive Pinterest’s core mission forward. What you will do: Technical Vision and Execution: Own the tech