Docker 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 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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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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As the Engineering Manager for Tool Development & Developer Experience, you will lead the team responsible for the MCP framework, tool catalog, and systems enabling customers to build tools. You will be ultimately responsible for the team's deliverables, stability, and uptime while aligning the team’s work with the product organization and shaping the team's and company’s roadmap. You will hire and mentor engineers, define and deliver new MCP servers, ship high-impact features ensuring reliability, security, and enterprise readiness, and build leverage into the system by automating tasks. While primarily leading people, product, and operations, you are expected to stay technically engaged through reviews, critical-path contributions, and occasional coding to unblock the team. The role involves navigating ambiguity, evolving AI tool standards, and managing scaling challenges.

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Define and execute product initiatives for agentic AI systems focusing on measurable customer and business outcomes. Own significant parts of the agentic system lifecycle including orchestration, decisioning, evaluation, and iteration. Contribute to building a repeatable framework for launching, evaluating, and improving agentic capabilities across customers. Help define how agentic systems are measured and improved in production balancing autonomy with safety and reliability. Partner closely with Engineering, Applied AI/ML, Design, and Solutions teams to ship production-ready systems. Work directly with customers to understand workflows, requirements, and success criteria. Drive customer-informed prioritization by staying close to live deployments and real usage patterns. Support best practices for agent evaluation, iteration, and safe rollout. Represent the product in customer conversations, demos, and feedback sessions.

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Engineers at Giga work on problems like building AI agents with almost no hallucination rates, creating a voice experience that is better than talking to humans, and creating self-learning agents that optimize metrics.

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Oversee the design and operation of the core platform including third-party providers, storage, billing, observability, security, and API. Provide technical leadership for various product and platform features. Improve developer experience to enable the whole team to ship faster. Guide efforts that bridge AI research to production across all modalities such as video, audio, image, and text. Understand the capabilities and limitations of state-of-the-art AI models and leverage them in products. Partner with product, design, and research teams to ensure development aligns with user needs and business objectives.