GCP AI Jobs

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The Software Data Engineer will collaborate with Machine Learning, Full-stack engineers, and Science teams to address complex document mining challenges and enhance the capture and modeling of scientific experiments. The role involves scaling data pipelines to transition data quickly and reliably from research to the platform, working with both semi-structured and unstructured data sources. Responsibilities include defining and applying best practices for a broad range of cloud-based technologies, architecting and maintaining robust data pipelines that ingest diverse data sources and use large language models (LLMs) for high-fidelity entity extraction into structured formats. The engineer will implement evaluation frameworks to monitor accuracy, drift, and hallucination rates of extraction models within production pipelines. They will lead or consult on engineering design proposals aligned with the unified Platform Stream roadmap, make independent technical decisions based on the business context, proactively identify and implement project improvements, respond urgently to operational issues, own issue resolution within their responsibility scope, and challenge current practices by proposing new technologies or working methods.

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Lead the design and implementation of core AI services including resilient fault-tolerant queues for task distribution, model catalogs for managing and versioning AI models, and scheduling mechanisms optimized for cost and performance. Architect and scale infrastructure to handle millions of API requests per second, implement robust monitoring and alerting for system health and 24/7 availability. Collaborate with product management, business strategy, and other engineering teams to define the AI platform roadmap, influence long-term vision and architecture decisions, contribute to open-source AI frameworks, actively participate in the AI community, and prototype and iterate on emerging technologies and new features.

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Lead the design and implementation of core AI services including resilient fault-tolerant queues for efficient task distribution, model catalogs for managing and versioning AI models, and scheduling mechanisms optimized for cost and performance. Architect and scale infrastructure to handle millions of API requests per second while ensuring robust monitoring and alerting for system health and 24/7 availability. Collaborate closely with product management, business strategy, and other engineering teams to define the AI platform roadmap, influence long-term vision and architectural decisions, contribute to open-source AI frameworks, participate in the AI community, and prototype and rapidly iterate on emerging technologies and new features.

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Co-architect and co-build production AI agents with customer engineering teams. Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations. Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows. Advise customers post-sale on architecture, best practices, and roadmap-level decisions. Run technical demos, trainings, and workshops for developer audiences. Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers. Occasionally contribute code upstream when it meaningfully improves customer outcomes.

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Lead major cross-team platform initiatives, taking foundational systems from 0 → 1 and scaling them to support millions of users. Build shared, extensible Agent primitives that Replit Agent can reuse safely and consistently. Identify the highest-leverage technical bottlenecks such as performance, reliability, correctness, abuse, and observability, then design and ship solutions for scale. Raise the bar for engineering excellence through architecture reviews, code quality, reliability standards, and mentorship. Partner across teams to improve platform adoption, ergonomics, and velocity, turning platform work into measurable outcomes. Work on core areas including Connectors framework, content/configuration primitives, data/analytics/events and experimentation primitives, Identity & Access platform, Localization/i18n platform, Notifications & communications platform, and core web platform infrastructure including performance optimization, observability, debugging workflows, caching strategy, and reliability.

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As a System Architect, you will own the end-to-end architecture, system definition, and strategic implementation for the entire portfolio of robotic systems, collaborating closely with executive leadership, technical leads, and the Product Manager to ensure efficiency. Responsibilities include translating complex strategic goals into global system-of-systems designs and defining the overall system architecture strategy across the enterprise. You will ensure all systems meet defined needs through verification of scope, complex simulations, and precise system sizing to guide major technical investments. Coordination and technical leadership involve managing large multidisciplinary engineering organizations and providing overarching technical leadership across cross-functional design efforts to ensure long-term performance, robustness, and strategic reliability. Additionally, you will govern system integration standards and validation processes, manage specification by ensuring architectural prerequisites are met, and drive multi-system architecture reviews for enterprise design consistency. You will also implement and institutionalize processes to enhance requirements traceability, system documentation standards, and validation workflows across the engineering organization.

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The Forward Deployed Engineer is responsible for writing and shipping production-grade code by designing, building, and deploying voice AI systems that power real enterprise workflows. They own deployments end-to-end, leading discovery, architecture, implementation, and rollout for strategic customer engagements. They are expected to drive progress through ambiguity by moving projects forward when requirements are incomplete, constraints evolve, or systems fail unexpectedly. The role involves delivering across complex infrastructure including cloud, VPC, and on-prem environments while addressing security, networking, and compliance requirements. The engineer must unblock critical integrations by diagnosing and resolving integration failures, performance bottlenecks, and deployment issues under real-world constraints. They drive expansion by identifying new use cases and prototyping solutions to deepen adoption and increase customer value. They also surface recurring patterns to engineering and product teams to influence the roadmap and reusable capabilities, and build for scale by transforming one-off solutions into repeatable playbooks, templates, and reference architectures.

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The Lead ML Engineer is responsible for architecting conversational agents that maintain long-running coherent dialogues and handle complex reasoning tasks. They develop low-latency retrieval augmented generation (RAG) pipelines that ground LLM responses in proprietary data to ensure accuracy and minimize hallucinations. The role leads the development of NLP pipelines to extract structured insights from various unstructured data sources, implements advanced personalization layers that adapt model behavior based on user history and context, and owns the deployment lifecycle of models including prompt architecture, evaluation frameworks, latency optimization, and cost management on Vertex AI. Additionally, the engineer acts as a technical mentor by reviewing code, setting architectural standards, and guiding technical decisions without managing personnel.

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The Lead Machine Learning Engineer will set the technical direction for complex ML projects, balancing trade-offs and guiding team priorities. Responsibilities include designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions. The role involves defining project problems, developing roadmaps, overseeing delivery across multiple work-streams in ill-defined, high-risk environments, and driving the development of shared resources and libraries across the organisation. The engineer will guide other engineers in contributing to these resources, lead hiring processes, make informed selection decisions, mentor multiple individuals to foster team growth, and develop and execute recommendations for adopting new technologies and changing working methods. Additionally, acting as a technical expert and coach for customers, accurately estimating large work-streams, and defending rationale to stakeholders is required.

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The Machine Learning Engineer is responsible for building and deploying production-grade machine learning software, tools, and infrastructure. They create reusable, scalable solutions that accelerate the delivery of ML systems. They collaborate with engineers, data scientists, and commercial leads to solve critical client challenges. They lead technical scoping and architectural decisions to ensure project feasibility and impact. They define and implement Faculty's standards for deploying machine learning at scale. Additionally, they act as technical advisors to customers and partners, translating complex ML concepts for stakeholders.