Java AI Jobs

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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.

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As a Full-Stack Engineer at Flowith, you will be responsible for independently or collaboratively leading the full-stack development of Flowith's core modules crossing front-end and back-end boundaries to deliver highly available and scalable system code. You will deeply integrate advanced AI algorithms and complex models into the product flow to create intelligent interactive experiences, work closely with product managers, designers, and AI engineers in a creative environment to implement innovative AI concepts, automate deployments and manage continuous integration on mainstream cloud infrastructure while monitoring and optimizing system performance and resource usage. Additionally, you will participate in the design evolution of the core architecture, conduct in-depth code reviews, and help accumulate technical components and best practices to elevate the engineering standards of the team.

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The intern will investigate challenging systems problems, build practical solutions, and measure their impact to improve Applied Systems by making them more efficient, scalable, and reliable. The work typically involves defining hypotheses about system improvements, instrumenting production systems to gather metrics and analyze data, building or modifying real systems with prototypes or production-quality improvements, running experiments and benchmarks, analyzing results, communicating tradeoffs and recommendations clearly, and publishing the research in technical journals and conferences. Focus areas include distributed systems and storage, compute and scheduling, performance engineering, reliability and observability, networking and data pipelines, and systems for machine learning.

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The responsibilities include designing, developing, and maintaining scalable, high-performance backend systems for an automated order-taking platform. The engineer will collaborate closely with the team to ensure seamless integration of backend systems with machine learning models and client devices. They will monitor and optimize backend system performance in production environments, build and maintain integrations with third-party restaurant software systems such as POS, loyalty, payment gateways, and customer data platforms. Responsibilities also include implementing best practices in system design, code quality, and testing to ensure a reliable, secure, and maintainable system; optimizing the AI pipeline to improve performance in challenging audio environments and handle ambiguous customer requests; pushing the boundaries of large language models (LLMs) and voice AI technology; and running experiments to validate the product impact of new functionality.

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Use software engineering experience to design job-related coding questions and review AI-generated responses for correctness, efficiency, clarity, and alignment with real-world engineering practices. Evaluate AI-generated code and technical content, provide structured feedback, and help improve AI's understanding of programming tasks, system design, and engineering best practices.

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Design, build, and maintain full-stack applications powering identity and access management (IAM) experiences. Develop and integrate AI/ML models for identity use cases such as fraud detection, anomaly detection, risk-based authentication, and identity verification. Lead and execute SSO migrations across products and platforms, consolidating authentication flows while minimizing user disruption. Drive domain consolidation initiatives by unifying identity systems, services, and user data models across multiple platforms or brands. Improve developer experience (DevEx) by building internal tools, SDKs, APIs, and documentation that simplify identity integrations. Design and evolve secure, scalable APIs supporting authentication, authorization, and identity data services. Partner closely with Security, Platform, and Product teams to implement and standardize protocols and patterns such as OAuth 2.0, OpenID Connect, SAML, JWT, and zero-trust architectures. Ensure AI-powered identity systems are observable, explainable, and production-ready, with robust monitoring and feedback loops. Balance security, performance, and usability while maintaining high standards for privacy and compliance. Contribute to architectural decisions, technical design discussions, and code quality standards.

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Design, build, and own the core backend systems, including services, data models, and business logic powering Clarion’s AI agents and healthcare workflows. Build infrastructure for multi-step, asynchronous workflows with conditional logic, retries, and failure handling. Own integrations with EHRs and legacy healthcare platforms via APIs and RPA. Create internal abstractions and tooling for deploying and customizing AI assistants for new customers. Implement and maintain backend authentication, authorization, and HIPAA-compliant architecture for enterprise healthcare.

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As a security engineer, applications at WRITER, you will build the security foundations protecting AI systems used by major brands. Responsibilities include conducting threat modeling sessions with product teams, designing secure architectures for new features, and ensuring security considerations are integrated from the start of product development. You will own and evolve the application security program, establish and maintain SAST/DAST scanning in CI/CD pipelines, perform security code reviews for critical changes, and build automation to detect vulnerabilities before production. You will partner with engineering teams to establish secure coding standards, create reusable security patterns and libraries, and design security features to protect customer environments. Integrating AI agents to improve security team efficiency, leading security assessments and penetration testing of applications, AI services, and APIs, as well as designing and implementing security controls for data pipelines, model training, and customer AI agents are also key duties. Additionally, you will research emerging threats specific to AI/ML security and build defenses against new risks.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing, both for internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Design and own the semantic layer that powers the system-of-record flywheel, enabling compounding AI products across teams. Build primitives, abstractions, and APIs for product teams to use as building blocks, ensuring ease of use for shipping AI-driven features. Partner closely with internal product and engineering teams to understand needs, eliminate friction, and design intuitive, well-documented systems that are hard to misuse. Architect systems that span data warehouses, OLTP databases, streaming systems, and vector stores, making tradeoffs based on latency, throughput, consistency, and access patterns. Work with leadership to define the long-term platform architecture, including build-vs-buy decisions, evolving the semantic layer, and scaling the system as product surface area grows.