AI Software Engineer Jobs

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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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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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You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios, work on impactful projects, and collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems.

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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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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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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.

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Design and own the semantic layer that powers the system-of-record flywheel, enabling compounding AI products across teams. Build primitives, abstractions, and APIs for product teams to use as building blocks, ensuring ease of use for shipping AI-driven features. Partner closely with internal product and engineering teams to understand needs, eliminate friction, and design intuitive, well-documented systems that are hard to misuse. Architect systems that span data warehouses, OLTP databases, streaming systems, and vector stores, making tradeoffs based on latency, throughput, consistency, and access patterns. Work with leadership to define the long-term platform architecture, including build-vs-buy decisions, evolving the semantic layer, and scaling the system as product surface area grows.

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Ship critical infrastructure managing real-world logistics and financial data for large enterprises. Own the why by building deep context through customer calls and understanding Loop's value to customers, pushing back on requirements if better solutions exist. Work full-stack across system boundaries including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to handle routine tasks enabling focus on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise velocity.