GCP AI Jobs

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Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

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Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.

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As an AI Tutor in Coding specializing in Kotlin development, the responsibilities include designing high-quality technical content, examples, and explanations demonstrating best practices in Kotlin development; collaborating with engineers to ensure accuracy and consistency across code samples, tutorials, and developer guides; exploring modern Kotlin frameworks and tools to create practical, real-world examples for learning and testing; and continuously refining content based on feedback, emerging patterns, and advances in the Kotlin ecosystem. The role also involves contributing to projects aligned with skills by creating training prompts and refining model responses to help shape the future of AI while ensuring technology benefits everyone.

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You will be building a new team in the US to develop the next generation smart AI video maker that can ingest user's content and compose quality videos for social media. You will work closely with product and marketing teams to quickly prototype, beta test, and produce the final version of this product using agent technology. The technology stack includes GCP, Typescript, Python, Redis, MongoDB, Cloud Storage, and various AI models. You will be involved in rushing prototype and production versions of this product, contributing to an innovative and ambitious project.

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The Software Engineer, ML Data Infrastructure will collaborate with engineers to build AI design experiences, tackle complex technical challenges including scaling distributed systems, build robust data infrastructure for foundation models at petabyte scale ensuring reliability and performance across multi-modal training pipelines, optimize data processing workflows for massive throughput, work with distributed systems, TPU infrastructure, and large-scale storage solutions, and partner with research scientists to translate data requirements into production-grade systems that accelerate model development cycles.

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The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

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The Solutions Engineer (AI/ML, Pre-Sales) will work closely with strategic customers to understand their data curation needs, business challenges, and technical requirements. The role involves leading end-to-end customer proofs of concept (PoCs) that connect data curation to training behavior and evaluation outcomes, including dataset analysis, training plan design, and interpreting results. They will partner with customer machine learning teams to map data and curation strategies, design and execute evaluation plans for base and post-trained models, select appropriate benchmarks and metrics, and run model evaluations. Additionally, the engineer will produce customer-ready evaluation reports detailing methodology, metrics, baselines, ablations (e.g., curated vs raw data), conclusions, and recommendations for productionization. They must communicate technical results effectively to both ML experts and executive stakeholders, explaining tradeoffs in compute, latency, and deployment cost. Collaboration with go-to-market, engineering, and research teams is essential to deliver compelling demos, align on requirements, and incorporate customer insights into model training and product strategies. The role also includes providing technical guidance, training, and documentation to enable prospects to confidently assess the solution.

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Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.

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Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.