AI Jobs in China

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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As an Applied AI Engineer, you will turn model capabilities into real product behavior by owning problems end-to-end, from shaping model behavior to building the systems around it and ensuring reliable performance in production. Responsibilities include building and shipping AI features end-to-end (model to system to user experience), designing and iterating on prompts, tools, memory, and agent workflows, turning raw model outputs into structured, reliable, and predictable behaviors, debugging issues across the full stack (model, orchestration, infrastructure, user experience), optimizing for latency, cost, and production reliability, developing lightweight evaluation frameworks to measure real-world performance, and working closely with product and engineering teams to translate ambiguous problems into working systems.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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Contributors may design rigorous electrical engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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Contributors design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients. Responsibilities include building and operating backend systems that serve AI-powered features in production, designing inference pipelines, orchestration layers, and service boundaries around models, owning production concerns such as monitoring, logging, alerting, and incident response, and optimizing latency and throughput across inference, caching, batching, and streaming.

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The role involves architecting, building, and scaling the internal technology ecosystem to accelerate workforce productivity, eliminate operational friction, and provide a compounding infrastructure advantage by treating internal tools with product rigor and user-centricity. Responsibilities include owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation; making strategic build-vs-buy decisions; mapping current and next-state process flows and leading systems transformation. The role requires architecting and maintaining the full engineering lifecycle of internal platforms, building API-first ecosystems integrating with various business systems, owning system reliability and operational resilience, and designing scalable, secure cloud-native architectures. The role leads AI adoption and automation integration into internal workflows, including deploying intelligent automation tools, evaluating AI-assisted troubleshooting, and driving continuous experimentation with prototypes. The person will reduce cognitive load for internal users by providing golden paths and standardized workflows, ensuring frictionless onboarding, and measuring platform success via adoption rates, user satisfaction, DORA metrics, and productivity impact. Team leadership duties include building, leading, and mentoring engineers and managers, fostering a collaborative culture rooted in ownership, speed, craftsmanship, and psychological safety. The role partners cross-functionally with various company leadership teams to translate business needs into a unified technical vision, aligning internal platform investments with company strategy and demonstrating measurable ROI.

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The Chief Technology Officer will define the long-term architecture for A1’s AI systems, infrastructure, and developer platform, evaluate trade-offs between speed of iteration and long-term system design, and ensure systems are designed for scalability, reliability, and long-term evolution. They will guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO will work with engineers to translate product direction into clear technical execution, help structure engineering workstreams and keep teams aligned on priorities, maintain high engineering standards while focusing on shipping, and establish engineering culture, development practices, and technical standards. Additionally, they will build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, work closely with product, research, and leadership teams, ensure technical workstreams move forward smoothly across teams and locations, and help resolve cross-team technical and execution challenges.

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Design and develop Flowith's Android platform products, focusing on architecture design and core development to ensure optimal app performance. Implement and optimize AI features for mobile applications, integrating advanced AI capabilities deeply on mobile devices. Collaborate with product, design, and backend teams to create exceptional user experiences that cross technical and business boundaries. Explore and implement cutting-edge Android technologies and frameworks continuously. Conduct code reviews and performance optimizations to maintain high code quality. Participate in Vibe Coding sessions, dedicating 70% to 99% of time to collaborative programming in a creative and dynamic environment.