GCP AI Jobs

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As a Data Engineer, you will build and evolve the data backbone of an AI-first product including document intelligence, time-series IoT data, and agentic AI systems. You will design, implement, and operate data systems across the full lifecycle from raw ingestion to AI-driven outputs used by customers. You will work directly with customers and internal stakeholders to understand problems and translate them into technical solutions, iterating quickly. Responsibilities include building pipelines that support document processing, sensor data, and ML workflows, contributing to feature engineering and model experimentation when needed, and owning systems in production. You will make architectural decisions, improve system reliability over time, and help define best practices as the team and product scale.

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The Software Engineer is responsible for utilizing the Agent Development Kit (ADK) to design, develop, and deploy autonomous agents and "skills" capable of multi-step data retrieval tasks. They design and develop backend systems and APIs to expose bioinformatics data and implement advanced search and retrieval mechanisms to provide LLMs with up-to-date grounded information. Their duties include tuning storage technologies, creating high-performance query plans, designing solutions, and adapting existing approaches to solve issues within web app architecture and interfaces. They operationalize production-grade data pipelines using processing engines like Apache Beam, collaborate with other engineers to address document extraction, enrichment, and retrieval challenges, and model scientific experiments from unstructured data. The engineer also troubleshoots and resolves production issues promptly, ensures code is testable, self-documenting, and reliable, communicates decisions to impacted teams, works on client-facing projects with large pharmaceutical companies, and balances independent work with collaborative efforts for complex architectural changes.

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The Engineering Manager for the Engine and Platform leads the team responsible for building, maintaining, and deploying the runtime for customers to run, manage, secure, and understand AI tools, enabling advanced agentic use-cases. This role involves scaling the team owning the development of the platform and services, which includes distributed systems engineers and authorization/identity experts developing features like MCP gateways, roles and permissions, and platform-as-service capabilities for tool executions. The manager ensures the team is unblocked, aligns the team's work with the product organization, and stays technically engaged through code reviews, critical contributions, and occasional hands-on coding. Responsibilities include owning deliverables, stability, and uptime, shaping product vision and architecture, owning technical direction and prioritization, hiring and mentoring engineers, defining and delivering platform features, and ensuring reliability, security, and enterprise readiness. The manager also focuses on building leverage into systems through automation and agents to improve efficiency and is expected to navigate ambiguity and evolving standards in AI tools.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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As a Full Stack Software Engineer at Replicant, you will design and deliver technology that powers natural, human-like conversations at scale to help companies reduce wait times, improve customer satisfaction, and empower representatives to focus on complex problems. You will build rich user experiences and backend services that enable customers to design, launch, and monitor AI-powered conversations. Responsibilities include building new features for Replicant's core AI voice and chat products handling millions of daily conversations, shipping full stack end-to-end features quickly, integrating automatic speech recognition, text to speech, and conversational AI model improvements into products, refactoring, optimizing, and debugging production systems balancing latency, cost, and user experience, participating in regular on-call rotations monitoring live systems, continuously improving systems based on performance metrics and customer feedback, shaping a culture emphasizing knowledge sharing and mentorship across distributed systems and enterprise-scale AI design, and participating in team and company-wide office events with travel required.

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Design, develop, and maintain web applications and backend services that integrate ML-powered features. Collaborate closely with Machine Learning Engineers and Product Managers to understand ML system requirements and translate them into robust software solutions. Build reliable, scalable, and low-latency services that support ML inference, data workflows, and AI-driven user experiences. Use LLMs to build scalable and reliable AI agents. Own the full software development lifecycle: design, implementation, testing, deployment, monitoring, and maintenance. Ensure high standards for code quality, testing, observability, and operational excellence. Troubleshoot production issues and participate in on-call or support rotations when needed. Mentor junior engineers and contribute to technical best practices across teams. Act as a strong cross-functional partner between product, engineering, and ML teams.

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The Evaluation Engineer will own the technical foundation of the auto-evaluation systems by building a comprehensive system that runs fast, is easy to use, and supports quickly building new evaluations. Responsibilities include improving the speed of the basic evals infrastructure with minimal latency, designing interfaces suitable for ML engineers, product managers, and customers, and ensuring the system architecture allows team members to easily add examples and run evaluations. The role also involves ensuring evaluations are accurate and reliable by encoding knowledge about how pharma customers make decisions, providing appropriate statistical tests, and confidence intervals for trustworthy results. Additionally, the engineer is expected to spend most time on the core eval platform, collaborate with the evals team on specific evals, mentor an evals engineering intern, and learn how users interact with the evaluation system to improve it.

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Build and operate high-throughput pipelines for sensor and event data (batch and streaming) ensuring quality, lineage, and reliability. Create scalable dataset curation and labeling workflows including sampling, slice definitions, weak supervision, gold-set management, and evaluation set integrity. Develop ML platform components such as feature pipelines, training orchestration, model registry, reproducible experiment tracking, and automated evaluation. Implement monitoring and observability for production ML systems covering data drift, performance regression, alerting, and automated failure detection. Standardize schemas and interfaces across studies and product telemetry to enable reusable, consistent analytics and model development. Collaborate cross-functionally with ML engineers, data science, firmware, and backend teams to support new studies and product launches, ensuring data architecture meets evolving research and product needs.

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Build tools for the full Physical AI data stack including data acquisition, ingestion, curation at scale, 3D rendering, annotation, and verification. Solve problems related to large scale efficient data transfer and storage, complex domain modeling, applying machine learning models, and optimizing performance in web browsers. Render complex 3D scenes with thousands of objects while maintaining smooth frame rates. Work autonomously on technical and product challenges, delivering performant, reliable, and maintainable solutions. Take end-to-end ownership of projects from product and design decisions through deployment, monitoring, and measuring impact on users. Work collaboratively in a small team environment and contribute to solving complex domains and scale with simple solutions.

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As a Senior Full-Stack Engineer at Encord, you will work on building tools for the complete Physical AI data stack, which includes data acquisition, ingestion, curation at scale, 3D rendering, annotation, and verification. You will solve problems related to large scale efficient data transfer and storage, complex domain modeling, applying machine learning models, and optimizing performance in web browsers. You will handle challenging front-end tasks such as rendering complex 3D scenes with thousands of objects while maintaining smooth frame rates. You will operate with a high degree of autonomy, crafting performant, reliable, and maintainable solutions to both technical and product challenges. You will have end-to-end ownership of your projects, from product design and architectural decisions to deployment, monitoring, and measuring impact on users, covering the full stack including deployment and styling. You will work closely in a small, highly collaborative team, often working autonomously to drive your projects forward while also collaborating and supporting your teammates. Your work will have a direct and tangible impact on customers and the company’s trajectory.