Java AI Jobs

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Design, develop, and operate backend systems for domain-specific collection search services including news, places, securities, sports, music, and movies. Design and standardize search architectures based on OpenSearch and MongoDB Atlas, including indexing and retrieval structures, to enable rapid expansion of new collections. Analyze search quality and maintain metrics such as nDCG, recall, MRR, and CTR to improve search accuracy, latency, and handle failure cases. Develop and fine-tune ranking models, reranking, embeddings, semantic search, and recommendation logic, focusing on top accuracy for priority collections. Build robust backend infrastructure required for stable production operation, including API contracts, caches, configuration registries, and administrative APIs. Lead technical decision-making processes, conduct design reviews within the team, and address complex problems by improving reusable systems.

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The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

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Translate business requirements into AI/ML model requirements. Prepare data to train and evaluate AI/ML/DL models. Build AI/ML/DL models using state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from research. Test and evaluate models, benchmark quality, and publish models, datasets, and evaluations. Deploy models in production by containerizing them. Work with customers and internal employees to refine model quality. Establish continuous learning pipelines for models with online or transfer learning. Build and deploy containerized applications on cloud or on-premise environments.

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Design, build, and operate backend systems that power image generation and image editing experiences in ChatGPT. Develop scalable APIs, services, and infrastructure that support multimodal AI workflows. Optimize reliability, latency, throughput, and cost across large-scale distributed systems. Partner with researchers to productionize new image generation capabilities and bring them to users quickly and safely. Collaborate closely with Android, iOS, web, and full-stack engineers to build seamless end-to-end product experiences. Drive technical architecture decisions across storage, serving, orchestration, and platform systems. Use data and experimentation to identify opportunities for improving user experience, performance, and system efficiency. Help shape engineering culture through technical leadership, mentorship, and operational excellence.

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As an Android Software Engineer, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. You will build and maintain production Android apps using Kotlin where AI interactions are core to the product. Responsibilities include integrating AI-powered features via backend APIs, designing UX patterns for AI interactions such as streaming responses, retries, and partial results, optimizing performance, memory usage, and responsiveness for AI-heavy flows, implementing analytics, logging, and feedback capture to support AI evaluation and iteration, collaborating closely with backend and ML engineers on API contracts and system behavior, and ensuring app stability, security, and scalability in production environments.

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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 for both 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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The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models by applying state-of-the-art algorithms such as transformers, leveraging existing algorithms from academic or industrial research, testing, evaluating, benchmarking the AI/ML/DL models, and publishing models, data sets, and evaluations. Additionally, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments are part of the role.

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Own the complete development lifecycle for spam and scam detection infrastructure including research, proposing solutions, implementation, testing, deployment, production maintenance, and monitoring. Participate in on-call rotation for rapid recognition and resolution of production issues while improving system reliability. Design and build frameworks that enable data scientists to develop, test, and deploy complex scam detection models with access to call data in a privacy-aware and regulation-compliant manner. Make independent implementation decisions while driving collaborative design discussions to improve system quality, maintainability, and cost-effectiveness. Evaluate critical tradeoffs between immediate fixes and durable solutions prioritizing service quality and system resilience. Collaborate with product managers, data scientists, and engineering teams to align technical decisions with business impact and user needs. Recognize and promote engineering patterns, design principles, and architectural decisions across teams to raise quality and execution speed. Influence team operations by pushing back on non-aligned solutions, surfacing issues early in project planning, and reasoning about business impact versus cost.

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The AI Infrastructure Supply Chain Lead is responsible for translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms such as transformers, testing and evaluating model quality, publishing models, data sets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.