Go AI Jobs

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Understand and identify the customer’s most important and impactful multi-step task to automate. Seamlessly integrate with customer systems, including but not limited to databases, third-party APIs, and internal tools. Rapidly iterate in the field with tight feedback loops to continuously improve product fit and drive end-to-end solution delivery. Architect, build, and maintain production-grade services that scale with usage and complexity. Collaborate closely with product, research, and engineering teams to shape the roadmap and align on priorities based on findings in the field.

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As the first US based Forward Deployed Engineer at Haast, you will act as the technical bridge between the product vision and customer needs. You will work as a full-stack engineer responsible for designing, architecting, and shipping full-stack features that solve customers' compliance challenges. Your role involves ownership of end-to-end technical decisions, including designing systems, shipping to production, and iterating on features based on direct customer interaction. You will maintain the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. Additionally, you will build scalable services and APIs for the LLM compliance platform with a focus on customer experience, make high-impact technical decisions aligned with engineering standards and customer needs, question assumptions about product development, and influence the product roadmap and engineering practices as the company scales from Series A to market leadership.

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As a Senior Software Engineer on the Platform - Cloud Events team, you will ensure that data captured by Hayden's devices is properly validated and assessed by ML models that run in the Cloud. You will improve ML operations by enabling a more efficient model improvement lifecycle, work closely with the Product Team, Technical Program Managers, and partner engineering teams to build edge software and interpret sensor data. Responsibilities include maintaining high code quality through code review and documentation, optimizing machine learning operations with robust systems and cross-team collaboration, expanding the event pipeline capabilities to meet general and client-specific needs, and reducing business costs by optimizing the usage of cloud resources, especially GPUs.

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As a Staff Engineer on the Orchestration team, you will drive the technical vision for Lambda's Managed Kubernetes bare-metal platform, including control plane scalability, multi-tenancy, cluster lifecycle management, and high availability. You will integrate and extend NVIDIA's open-source ecosystem, design GPU-aware orchestration systems, and lead the development of services powering managed services. Your responsibilities include informing and helping with networking solutions such as CNI integration and high-performance fabrics, and informing and helping with storage architecture requirements for AI workloads. You will build the foundation for Managed Slurm on Kubernetes, design higher-level platform services for inference, including model serving infrastructure and autoscaling, and design self-healing systems and automation for incident response and platform resilience. You will lead chaos engineering efforts to validate system behavior under failure conditions, establish operational excellence including upgrade automation and zero-downtime maintenance. Additionally, you will serve as a technical bridge between Orchestration and other infrastructure teams, drive infrastructure-wide decisions, provide input on bare-metal provisioning, network topology, and storage systems, champion consistency and standardization, work directly with customers and internal teams to understand deployments and roadmap managed platforms. Your role includes setting technical direction for Kubernetes services, driving reviews and design sessions, mentoring engineers, collaborating cross-functionally, engaging with NVIDIA and open-source communities, representing Lambda externally, and shaping AIOps vision for automated capacity planning, anomaly detection, and predictive maintenance of cloud infrastructure.

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The Senior Staff Data Center Operations Engineer, GPU Hardware Architecture is responsible for providing deep technical guidance to the Data Center Engineering team regarding upcoming silicon such as NVIDIA Blackwell/Rubin and AMD MI350/400 to ensure future facility designs meet power, cooling, and rack-spacing needs for high-density GPU architectures. They develop diagnostic tools and precision SOPs to enable Site Operations technicians to identify and resolve complex GPU hardware issues accurately. The role involves using AI/ML methodologies to analyze fleet-wide telemetry for predictive maintenance, identifying pre-failure patterns before they impact operations. Additionally, they define technical sparing requirements and site-level inventory strategies based on hardware failure telemetry and MTBF data to meet uptime targets. The engineer acts as a Tier-3 escalation point for complex hardware failures and leads root cause analysis on systemic hardware and facility environmental issues. They maintain a 24-month forward-looking view on GPU architectures and educate internal stakeholders on impacts to infrastructure. The role also includes supporting vendor and VAR relationships by auditing hardware builds and ensuring alignment with Crusoe’s standards.

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Design, build, and scale AI-powered, user-facing products that enhance Eva's healthcare interactions. Lead end-to-end delivery of complex full-stack product experiences including frontend architecture, backend services, APIs, data integrations, and production workflows. Collaborate with Product, Design, Data Science, Infrastructure, and other engineering teams to integrate AI capabilities into user workflows. Translate product and AI opportunities into clear technical plans balancing speed, user experience, reliability, safety, and long-term maintainability. Build robust systems connecting application logic, data pipelines, model outputs, and human-centered product experiences. Evaluate and integrate AI technologies such as LLM-enabled workflows, retrieval systems, classification, summarization, automation, and decision-support experiences. Establish frontend and backend engineering standards, including component architecture, API design, system reliability, observability, testing, and development best practices. Make technical tradeoffs considering product impact, engineering complexity, scalability, and platform health. Drive cross-team and cross-organization alignment through technical clarity and collaboration. Mentor and grow engineers, improving technical execution, product thinking, and engineering culture. Contribute to technical roadmaps focusing on AI-enabled product development, platform scalability, and full-stack architecture. Help shape Eva's responsible AI use emphasizing usefulness, trust, reliability, and user experience.

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Product Engineers at Fluidstack work on the AI-integrated software stack to build capacity rapidly without handoffs or ticket implementation. They own features from concept to outcome, including tasks such as supply chain automation for large-scale GPU ordering, capacity forecasting and scheduling systems, development of internal AI infrastructure and integrations, operational automation to reduce manual toil, and datacenter design and validation applications. They are responsible for identifying problems, designing solutions, building, shipping, and measuring success with high levels of ownership and autonomy, operating infrastructure worth billions with rapid timelines, and reducing time to compute.

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Design, architect, and ship full-stack features that directly solve customers' compliance challenges. Own the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. Build scalable services and APIs that power the LLM compliance platform while prioritizing the customer experience. Make high-impact technical decisions quickly, accountable to engineering standards and customers. Challenge assumptions about product development and influence what is built and how. Shape the product roadmap and engineering practices during the scale from Series A to market leadership. Split time between building code and engaging with customers, understanding pain points, iterating on features in real-time, and shaping product direction. Own end-to-end technical decisions: designing systems, shipping to production, and iterating based on customer interaction. Collaborate directly with the founding team and have influence over architecture and product roadmap.

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Build the infrastructure to manage the backplane for all future AI tools; design, build, test, and deploy high-performance, secure, production-grade software; develop SDKs and frameworks that make it easy for other developers to build custom tools; help set industry standards for tool calling and authorization; create integrations with major platforms and LLMs such as Google Workspace, Microsoft 365, OpenAi, Anthropic; shape the roadmap for the team; build leverage via AI to reduce development time; share work with customers and community to build brand recognition.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team’s capabilities. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.