Kubernetes AI Jobs

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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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As a Software Engineer and member of the Platform Stability team, you will help build, fine-tune, and maintain a novel AI-powered tool for diagnosing technical issues and identifying root causes. You will collaborate cross-functionally to gather requirements, develop AI/ML and analytical models, and drive data-driven insights as part of a high-performing team. Responsibilities include designing and implementing agentic AI systems with structured interfaces, reasoning loops, and robust error handling; building and maintaining data pipelines, scheduled workflows, and benchmarking infrastructure; developing evaluation and scoring systems to measure and improve model output quality; integrating the platform with internal and external services such as ticketing, messaging, storage, and observability; collaborating with cross-functional teams to translate business requirements into technical AI solutions; and architecting and maintaining production-grade AI solutions with a focus on scalability, reliability, and performance.

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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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As a Software Engineer, Agents, you will build systems that make AI agents indispensable to legal professionals by designing environments and actions for agentic professional work, making model selection decisions, managing context windows, creating optimal tools, and developing evaluation harnesses for faster iteration loops to unlock new capabilities. You will partner with customers and product managers to understand legal workflows, design practical evaluations to capture what excellence means, and ship agents that effectively complete tasks. Additionally, you will optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and evaluation harness development. You will work with the model infrastructure team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns. Improving observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions is also part of the role. Staying current on new developments in agentic systems and applying those insights to product development is expected.

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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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As a Guidance and Controls engineer, you will be responsible for creating and maintaining all control and autonomy algorithms within the XBAT code base. This includes algorithm development, unit tests, component tests, flight software qualification, and flight test support. You will also be responsible for helping update and validate the truth models as required.

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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.