GCP AI Jobs

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As a Senior Staff AI Engineer at Hippocratic AI, you will define the multi-year technical strategy and architectural roadmap for the AI platform encompassing RAG, multi-agent systems, real-time voice, evaluation, and safety, aligning leadership, engineering, and research around it. You will architect foundational platforms and systems used across multiple products, teams, and partners, making critical technical decisions for the company's future. You will partner directly with executive, product, and clinical leadership to translate long-term healthcare goals into technical initiatives. Additionally, you will represent engineering in board-level, partner, and regulatory discussions, identifying and driving both zero-to-one and one-to-n innovations that expand the company's capabilities. You will own the company-wide safety and evaluation standards, setting gates and measurement systems that all agents, partner deployments, and model changes must pass. Furthermore, you will mentor Staff and Senior engineers, influence hiring and leveling, multiplying the impact of engineering teams, and represent Hippocratic AI externally at conferences, through publications, and partner engagements in the healthcare AI community.

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As a Staff AI Engineer at Hippocratic AI, you will set the technical direction for voice-based generative AI in healthcare, architect intelligent systems powering clinically safe healthcare agents, and own one or more core AI domains end-to-end including RAG, agent orchestration, evaluation, or real-time voice. Responsibilities include designing foundational production-grade AI pipelines for voice-based generative healthcare agents incorporating multi-step reasoning, agent orchestration, and evaluation systems; leading cross-functional initiatives with product, clinical, and engineering teams to translate healthcare workflows into safe and scalable AI experiences; representing engineering in clinical and partner conversations; driving innovation using state-of-the-art LLMs, retrieval systems, and streaming architectures; setting standards for AI-native workflows supporting real-time, conversational, and long-running interactions across healthcare contexts; owning safety and evaluation standards across model evaluation, safety testing, and observability; defining production gates for agents; mentoring senior and mid-level engineers; and elevating team quality through code and design reviews.

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Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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The Lead Member of Technical Staff, Inference Infrastructure, is responsible for providing technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments. They lead the design of customized deployments to meet specific customer needs and mentor engineers to raise the technical standards across the team. The role involves contributing to the development, deployment, and operation of the AI platform delivering large language models through easy-to-use API endpoints, and serving as a key point of contact for customers.

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As a Staff Software Engineer on the Core Infrastructure team at Harvey, your responsibilities include designing and building scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions. You will own and evolve the multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management. You will lead technical initiatives focused on observability, incident response, and operational excellence, building systems for rapid detection and resolution of issues. Architecting and optimizing distributed systems for reliability, including load balancing, quota management, and failover mechanisms, will be part of your role. You will partner with Product Engineering and Security teams to ensure infrastructure accelerates product development, drive infrastructure-as-code practices using tools like Terraform and Pulumi for reproducible deployments, and mentor engineers through code reviews, design reviews, and technical leadership. Representative projects include designing model proxy architecture for handling inference requests, building distributed rate limiting and quota management systems, architecting multi-region deployment strategies for data residency compliance, developing observability infrastructure with SLA monitoring and cost tracking, and leading CI/CD pipeline evolution to improve velocity and stability.

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Develop systems and tooling to measure, monitor, and improve token throughput across first-party and partner-owned compute environments. Support performance benchmarking, tokenomics analysis, and model porting across heterogeneous infrastructure environments. Build tooling to integrate external or partner infrastructure into OpenAI’s internal compute, observability, and workload management systems. Develop and monitor operational metrics including billing, usage, SLAs, utilization, reliability, and throughput. Identify bottlenecks across hardware, networking, software, and workload enablement that prevent capacity from becoming productive tokens. Partner with compute, infrastructure, networking, finance, and operations teams to translate raw capacity into usable workload-serving capacity. Build dashboards, automation, and reporting systems that provide clear visibility into TaaS capacity, performance, and business outcomes.

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As a Software Engineer on the Coding Pod, you will build the data infrastructure and pipelines that power frontier AI coding models. Responsibilities include designing and building scalable data pipelines for generating, transforming, and validating large-scale coding datasets; developing systems for task generation, dataset curation, and quality assurance, including automated and human-in-the-loop evaluation workflows; integrating with developer ecosystems such as GitHub and building tooling to support real-world coding environments; working with containerized environments like Docker to safely execute and evaluate code at scale; building backend systems and APIs that power dataset delivery and model evaluation pipelines; collaborating closely with ML researchers, product managers, and other engineers to define evaluation methodologies and improve dataset quality; implementing automated grading, benchmarking, and assessment systems for coding tasks; debugging and optimizing pipeline performance, reliability, and scalability across distributed systems; and contributing to architectural decisions around data infrastructure, evaluation systems, and pipeline orchestration.