AWS AI Jobs

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As a Software Engineer on the AI Voice Agent team, you will work on real-time speech pipeline systems including live audio buffering, streaming, latency optimization, and integrating with speech providers. You will build and improve conversation intelligence systems that manage the LLM layer for natural conversation flow, including prompt construction, context management, function calling, and dialogue management. You will develop the action framework that allows the AI Voice Agent to execute tasks during calls such as querying account data, creating tickets, and checking order status, handling API configuration, success/failure branching, authentication management, and runtime execution. Additionally, you will work on knowledge ingestion, storage, and retrieval for the voice agent and manage memory for retaining information across conversations to improve responses. You will collaborate with designers to create easy-to-use interfaces for agent lifecycle management including creation, configuration, testing, and deployment. You will contribute to building evaluation frameworks and metrics for voice AI quality, post-call analytics, and instrumentation, as well as participate in the on-call rotation.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio such as buffering, streaming, and latency optimization, along with integrating speech providers. You will build and improve conversation intelligence systems, including prompt construction, context management, function calling, and dialogue management to make conversations feel natural. You will develop the action framework to execute configurable API calls, manage success/failure branching, authentication, and runtime execution during calls. You will work on knowledge ingestion, storage, retrieval, memory, and context for the voice agent to improve its performance over time. Additionally, you will collaborate on agent lifecycle tasks such as creation, configuration, testing, and deployment of voice agents and help build evaluation frameworks for model performance, call quality metrics, and call analytics. Participation in on-call rotations is also expected.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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As a Staff Software Engineer in the Foundations department, responsibilities include leading the design and implementation of highly scalable systems for the Managed AI offerings, driving the long-term technical roadmap for the Foundations team to support growth and evolving AI workloads, working cross-functionally with Cloud Engineering to align technical goals and solve integration challenges, leading by example through high-quality code contributions and mentoring Senior and Staff-level engineers, championing reliability, observability, and performance by identifying and resolving systemic bottlenecks, and staying current with AI infrastructure trends to ensure efficient and powerful tools are utilized.

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark AI/ML/DL models and publish the models, data sets, and evaluations; deploy models in production by containerizing them; work with customers and internal employees to refine model quality; establish continuous learning pipelines for models using online or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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The AI Engineer will design and develop intelligent agents powered by large language models (LLMs) using tool calling, orchestration frameworks, and advanced context management to enable reasoning, planning, and autonomous decision-making across complex workflows. Responsibilities include working hands-on with modern agentic stacks such as LangGraph and Autogen, implementing asynchronous and streaming architectures, and ensuring production-grade observability to build scalable real-world AI systems.

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The Tech Lead Manager, Data Infrastructure at Cartesia is responsible for defining the overall multi-modal data strategy across pre-training and post-training, including human, synthetic, and web-scale data sources. They lead, manage, and mentor a team of data engineers and specialists. They design and oversee the construction of robust, scalable data pipelines for text, audio, and video, establish and enforce rigorous standards for data quality across the organization, deeply understand how data affects model capability and proactively identify and source novel datasets, and manage relationships and budgets with external data vendors and partners.

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Drive the end-to-end technical deployment of Latent Labs models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure. Design and build production-grade API integrations, data pipelines and model-serving infrastructure tailored to each customer’s requirements. Work on-site or embedded with pharma and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions. Ensure deployments meet enterprise standards for security, performance and reliability. Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including spending time working on-site at international partner locations as needed. Gather and synthesise customer feedback, translating it into actionable insights for product, research and platform teams. Collaborate with internal teams to shape the product roadmap based on real-world deployment learnings. Create technical documentation, integration guides and best-practice resources for customers. Stay on top of the latest developments in ML infrastructure, model serving and cloud-native tooling. Gain a strong working understanding of protein and cell biology as it relates to the product. Participate in knowledge sharing, including organizing and presenting at internal reading groups.

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The responsibilities include advancing inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implementing and maintaining changes in high-performance inference engines, including kernel backends and speculative decoding, profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unifying inference with RL/post-training by designing and operating RL and post-training pipelines, making RL and post-training workloads more efficient with inference-aware training loops, and using these pipelines to train, evaluate, and iterate on frontier models. Co-designing algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Running ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and feeding these insights back into model, RL, and system design. Owning critical systems at production scale by profiling, debugging, and optimizing inference and post-training services under real production workloads, driving roadmap items requiring engine modification, and establishing metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Providing technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training, and mentoring other engineers and researchers on full-stack ML systems work and performance engineering.

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, placement and routing (P&R), static timing analysis (STA), signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results (QoR). Optimize EDA tools and custom CAD flows using data-driven and machine learning-based techniques, working closely with internal teams such as verification, extraction, timing, Design for Test (DFT), and electronic design automation (EDA) vendors.