AWS AI Jobs

View detail

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.

View detail

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.

View detail

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.

View detail

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.

View detail

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.

View detail

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.

View detail

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.

View detail

Own the technical foundation of Meeno end-to-end including web, mobile, backend, data, and experimentation. Co-design product vision in close partnership with Meeno's team. Build core AI product primitives such as voice capture/playback, low-latency interactions, scene framework (content, branching, scoring hooks), feedback loops and user progression, and personalization. Architect systems for speed and iteration with weekly experiments rather than quarterly releases. Set the engineering standards for quality, reliability, security/privacy, and shipping culture. Hire and mentor engineers as the team scales, focusing on quality over quantity and leveraging AI and talent to maintain lean operations.

View detail

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.

View detail

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.