<p><strong>About the Role</strong></p><ul><li>We are building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organizing and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.</li><li>As Technical Lead, Machine Learning, you will own the execution layer of our intelligence. You will translate research direction into reliable, scalable, production-grade ML systems.</li><li>This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints.</li></ul><p> </p><p><strong>What You'll Do</strong></p><ul><li>Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.</li><li>Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.</li><li>Architect and operate scalable inference systems, balancing latency, cost, and reliability.</li><li>Design and maintain data systems for high-quality synthetic and real-world training data.</li><li>Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.</li><li>Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.</li><li>Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.</li><li>Make pragmatic trade-offs and ship improvements quickly, learning from real usage.</li><li>Work under real production constraints: latency, cost, reliability, and safety.</li></ul><p> </p><p><strong>Requirements</strong></p><p>Outcomes</p><ul><li>Research and models reliably translate into production-ready solutions with clear performance and quality targets.</li><li>ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.</li><li>Production issues are detected, debugged, and resolved quickly, minimizing user impact.</li><li>Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.</li><li>Iterations on models and systems are measurable, safe, and improve user experience over time.</li></ul><p> </p><p>Tech Stack</p><ul><li>Python</li><li>PyTorch / JAX</li><li>GPU-based training and inference system</li></ul><p> </p><p>Ideal Experience</p><ul><li>You have built or shipped real ML systems used by people, not just demos.</li><li>You are comfortable working with large models and understanding their failure modes.</li><li>You write strong, production-grade code and care about system correctness.</li><li>You are self-directed, pragmatic, and take full ownership of outcomes.</li><li>You communicate clearly and collaborate well in small, high-trust teams.</li></ul><p> </p><p><strong>How We Work</strong></p><ul><li>The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product.</li></ul><p><em>By clicking 'apply', you give your express consent that Robert Half may use your personal information to process your job application and to contact you from time to time for future employment opportunities. For further information on how Robert Half processes your personal information and how to access and correct your information, please read the Robert Half privacy notice https://www.roberthalf.cn/en/privacy-statement. Please do not submit any sensitive personal data to us in your resume (such as government ID numbers, ethnicity, gender, religion, marital status or trade union membership) as we do not collect your sensitive personal data at this time.</em></p><hr /><p><em>点击"申请"，即表示您明确同意 Robert Half 可以使用您的个人信息来处理您的工作申请，并不时与您联系以获得未来的就业机会。 如需进一步了解 Robert Half 如何处理您的个人信息以及如何访问和更正您的信息，请阅读 Robert Half 隐私声明<a href="https://nam02.safelinks.protection.outlook.com/?url=https://www.roberthalf.cn/en/privacy-statement&amp;data=05|01|[email protected]|45960615caf741a897da08db12e2448b|16532572d5674d678727f12f7bb6aed3|0|0|638124535281002861|Unknown|TWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0=|3000|||&amp;sdata=txey6FkFkoVyheBOucHzeYO1yTcFywlr7N4oX7M8axY=&amp;reserved=0">https://www.roberthalf.cn/en/privacy-statement</a> 。请不要在您的简历中向我们提交任何敏感的个人数据（例如身份证号码、种族、性别、宗教、婚姻状况或工会会员身份），因为我们此时不收集您的敏感个人数据。</em></p><p> </p><p> </p><img src="https://counter.adcourier.com/QWlkZW5YaWEuNDIyMzcuMTA5MjFAcmhpY24uYXBsaXRyYWsuY29t.gif">