Kubernetes AI Jobs

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Translate research into product by working with client-side researchers on post-training, evaluations, safety/alignment, and building necessary primitives, data, and tooling. Partner closely with core customers and frontier AI labs to address complex technical problems related to model improvement, performance, and deployment. Shape and propose model improvement work through clearly defined technical proposals, scope of work, and execution plans. Own the end-to-end lifecycle of projects, including discovery, writing product requirement documents (PRDs) and technical specifications, prioritizing trade-offs, conducting experiments, shipping solutions, and scaling pilots into repeatable offerings. Lead high-stakes technical engagements by running working sessions with senior stakeholders, defining success metrics, identifying risks early, and driving programs to measurable outcomes. Collaborate across multiple departments such as research, platform, operations, security, and finance to deliver reliable production-grade results. Build rigorous evaluation frameworks, maintain data quality feedback loops, and share insights to improve technical execution across accounts.

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The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, building the necessary primitives, data, and tooling. The engineer partners closely with leading AI teams and frontier research labs to solve complex technical problems related to model improvement, performance, and deployment, shaping and proposing technically rigorous model improvement work. Responsibilities include leading the end-to-end lifecycle from discovery to scalable pilots, conducting technical working sessions with senior stakeholders, defining success metrics, managing risks, and driving programs to measurable outcomes. The role requires collaboration with research, platform, operations, security, and finance teams to deliver reliable, production-grade solutions. Additionally, the engineer designs and establishes robust evaluation frameworks, closes feedback loops on data quality, and shares best practices across accounts.

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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 role involves partnering closely with ML teams and AI research teams to translate research needs related to post-training, evaluations, safety/alignment into clear product roadmaps and measurable outcomes. Responsibilities include working hands-on with leading AI teams and frontier research labs to tackle technical problems in model improvement and deployment, shaping and proposing model improvement work by translating objectives into well-defined statements of work and execution plans, and collaborating on designing data, primitives, and tooling required to improve frontier models in practice. The position also requires owning the end-to-end lifecycle of projects, including discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling successful pilots into repeatable offerings. Leading complex, high-stakes engagements by running technical working sessions with senior stakeholders, defining success metrics, surfacing risks early, and driving programs to measurable outcomes is part of the role. Additionally, the role requires partnering closely across research, platform, operations, security, and finance to deliver production-grade results for demanding customers and building rigorous evaluation frameworks such as benchmarks and RLVR to improve technical execution across accounts.

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As a Software Engineer on Consumer Products Research, the responsibilities include building and shipping production software for sensing algorithms by translating algorithm prototypes into reliable end-to-end systems, implementing and owning key parts of the Python shipping pipeline including integration surfaces, evaluation hooks, and quality/performance guardrails. The role also involves developing embedded/on-device software in an RTOS environment (such as Zephyr) and deploying models to device runtimes and hardware accelerators. Additional responsibilities include optimizing real-time on-device perception loops for stability, latency, power, and memory constraints, creating data collection and instrumentation tooling to bring up new sensing modalities and accelerate iteration from prototype to dataset to model to device, and partnering cross-functionally with algorithms, human data, firmware/hardware teams to debug, profile, and harden systems against real-world variability.

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The role involves developing machine learning and artificial intelligence systems by leveraging and extending state-of-the-art methods and architectures, designing experiments, and conducting benchmarks to evaluate and improve AI performance in real-world scenarios. The candidate will participate in impactful projects and collaborate with multiple teams and backgrounds to integrate cutting-edge ML/AI into production systems. Responsibilities also include ensuring AI software is deployed to production with proper testing, quality assurance, and monitoring.

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Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency. Build tooling and establish development best practices to manage and upgrade foundational libraries such as Nvidia driver, PyTorch, TensorRT, to improve ML developer experience and expedite debugging efforts. Collaborate closely with cross-functional teams including applied ML research, high-performance compute, advanced hardware engineering, and data science to define requirements and align on architectural decisions. Work across multiple ML teams within Zoox, supporting in- and off-vehicle ML use cases and coordinating to meet the needs of vehicle and ML teams to reduce the time from ideation to productionization of AI innovations.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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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.