GCP AI Jobs

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As the Senior Director of Engineering for Developer Experience at Crusoe, you will own and drive the strategy, execution, and culture of the team responsible for how Crusoe's engineers and non-engineers build, ship, and operate software. Responsibilities include defining and executing the long-term vision for Crusoe's internal developer platform, which encompasses shared services, internal APIs, repositories, and self-service infrastructure to enable engineering teams to move quickly and confidently. You will also rapidly develop and productionize AI-powered tools for the entire company, creating and evangelizing best practices for productionizing AI-developed tools and evaluating SaaS purchases. Additionally, you will oversee the design, reliability, and continuous improvement of CI/CD pipelines, build systems, and deployment infrastructure to ensure safe and rapid scaling of engineering teams' shipping processes. Your role will also involve defining and driving organization-wide engineering productivity initiatives by establishing metrics, identifying bottlenecks, and implementing tooling and process improvements that enhance developer experience across Crusoe. People leadership is a key responsibility, including managing and growing a team of engineers and fostering a high-performance culture based on accountability, innovation, and continuous learning. Furthermore, you will collaborate with senior leaders across Engineering, Infrastructure, Security, and Product to align Developer Experience investments with company-wide engineering goals and priorities.

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The Deployed Engineer will 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 including conversational agents, research agents, and multi-step workflows, and advise customers post-sale on architecture, best practices, and roadmap-level decisions. They will also run technical demos, trainings, and workshops for developer audiences, surface field feedback, contribute reusable patterns, cookbooks, and example code that scale across customers, and occasionally contribute code upstream when it meaningfully improves customer outcomes.

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Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.

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Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints, translate model capabilities, data limitations, and evaluation results into clear product decisions, make trade-offs across quality, latency, cost, reliability, and user experience, work daily with ML, backend, and mobile engineers on design, evaluation, and iteration, define success metrics and feedback loops across offline evaluation, online experiments, and human feedback, drive execution with clear specifications, risk awareness, and disciplined prioritization, ensure AI features ship quickly, safely, and reliably into production, and own AI product quality across UX, correctness, and outcomes.

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Build and ship features for North, an AI workspace platform; develop autonomous agents that interact with sensitive enterprise data; experiment rapidly and with high quality to engage customers and deliver solutions that exceed expectations; work across the entire product lifecycle from conceptualization through production; lead end-to-end deployment of North in private cloud and on-premises environments including planning, configuration, testing, and rollout.

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As a Solutions Engineer at Encord, you will be the core technical expert for customers building autonomous vehicles, robotics, and physical AI solutions, specializing in LiDAR data, sensor fusion, and perception. Your responsibilities include leading technical discovery with perception teams to understand their sensor stacks, model development pipelines, and data challenges; architecting complete solutions for complex multimodal datasets including LiDAR, camera, and radar fusion, and sensor calibration; acting as the technical authority on handling 3D point clouds, sensor fusion, temporal sequences, and multimodal annotation; building bespoke proofs of concept for LiDAR data ingestion, point cloud processing, coordinate transformations, and sensor calibration; developing custom integrations with robotics/AV stacks such as MCAP, ROS, Apollo, and Autoware; creating technical demos for LiDAR annotation, 3D bounding boxes, semantic segmentation, and multi-sensor fusion; debugging complex issues involving point cloud rendering, sensor calibration matrices, and multimodal data synchronization; guiding prospects through technical evaluations of LiDAR formats, sensor configurations, and annotation requirements; providing expert consultation on 3D annotation best practices, coordinate conventions, and quality control workflows; partnering with Account Executives to co-own technical wins in enterprise sales cycles; translating technical capabilities into business value for CTOs and senior stakeholders; and channeling customer feedback to Product and Engineering teams to shape the product roadmap.

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Build systems that integrate with the EHRs used in American healthcare to make Heidi feel like a native capability rather than a plugin. Develop systems that simplify the complexity of US healthcare billing, compliance, and payer constraints so clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability, ensuring the workflows are reliable for clinicians, operators, and downstream systems. Focus on outcomes by ensuring that the built systems help clinicians and improve practice revenue. Create agentic workflow functionalities where AI assists with extraction, reconciliation, and drafting within workflows, incorporating human review, auditability, and control. Collaborate closely in a team environment with frequent pairing and shared ownership of design and implementation. Learn about healthcare organizational operations, especially those serving US customers, to translate requirements and constraints into product improvements.

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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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The Head of Engineering at Lazo is responsible for owning the technology strategy and roadmap aligned with business and product OKRs, defining the reference architecture for agentic systems including LLMs and tool orchestration, establishing security and compliance baselines such as IAM, data privacy, and SOC2-readiness, and managing cost governance (FinOps). They present trade-offs, risks, and progress in leadership reviews. The role involves hands-on engineering and delivery, including shipping backend services in Python/TypeScript, orchestrating agents and toolchains, integrating external APIs and databases, building robust pipelines, and handling end-to-end DevOps using AWS/GCP, containerization, IaC, CI/CD, and observability, as well as on-call design. They work to reduce technical debt, improve latency and throughput, and manage infrastructure cost per workflow/client. Responsibilities also include defining SLOs and error budgets to reduce MTTR and change-fail rates, implementing data access policies and secure data flows for AI features, driving post-mortems and preventive engineering, hiring and mentoring engineers, setting performance scorecards, fostering a culture of thoughtful trade-offs and fast feedback loops, partnering with Product and AI teams for scalable solutions, collaborating with Ops, Growth, and Customer teams for reliability and launch readiness, and managing key vendors and build-versus-buy decisions with ROI narratives.

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Run technical discovery, design solutions, and lead POCs alongside Account Executives to close deals, then own onboarding to get customers to first value fast. Build and implement workflows within V7 Go; combining prompt engineering, data pipelines, and integrations to solve real customer problems across document processing and more. Act as the primary technical contact for accounts, handling complex challenges and spotting expansion opportunities as customers scale. Juggle up to 10 concurrent projects while feeding customer insights back to product and engineering.