AI Software Engineer Jobs

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Build and improve production RAG and LLM-based systems. Design and maintain data pipelines that integrate enterprise data sources at scale. Develop full-stack product features end-to-end, from Python backend through React/TypeScript frontend. Work on optimization and scheduling problems with real operational constraints. Build evaluation frameworks that measure whether AI systems are actually improving.

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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, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.

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As a Product Backend Engineer, you will design and operate backend systems that enable AI capabilities to provide dependable product experiences. Responsibilities include collaborating closely with Product to prioritize customer-focused work and deliver reliable features quickly, designing and owning backend services and APIs for web applications, workflows, and integrations, modeling and managing data in Postgres and related data stores, building secure, multi-tenant, permissions-aware systems with appropriate auditing, implementing backend features using LLMs and agentic tools, collaborating with frontend, product, and design teams to define API contracts and ship features end-to-end, adding logging, metrics, and tracing for service observability and on-call readiness, improving performance and scalability by profiling and tuning, and participating in code reviews, technical design discussions, and an on-call rotation for the services you own.

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As a Product Backend Engineer, design and operate backend systems that enable AI capabilities to deliver seamless and dependable product experiences. Build secure, multi-tenant services, orchestrate interactions with large language models (LLMs) and agentic tools, and define backend architecture for expanding product offerings. Collaborate with Product to prioritize customer-focused work and deliver reliable features quickly. Design and own backend services and APIs supporting web applications, workflows, and integrations. Model and manage data in Postgres and related data stores to ensure low-latency and reliable user experiences. Build permissions-aware systems with appropriate auditing for enterprise and government customers. Implement backend features that interact with AI systems via robust, well-structured services. Collaborate with frontend, product, and design partners for solution design, API contract definition, and end-to-end feature delivery. Add logging, metrics, and tracing for service observability and on-call readiness. Improve performance and scalability by profiling, tuning, and refining service boundaries. Participate in code reviews, technical design discussions, and an on-call rotation for owned services.

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The Application Engineer will dream up, build, and ship LM Studio features to millions of users worldwide, working swiftly. Responsibilities include intermixing UI work with systems engineering, design, and applied AI/agentic engineering work. The role requires a holistic understanding of software systems and the ability to work across the stack, involving building software products and crafting product experiences.

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As a Senior Software Engineer, Agents, you will dream up, build, and ship LM Studio features to millions of users worldwide at a fast pace. Your work will involve intermixing UI work with systems engineering, design, and applied AI / agentic engineering. You will be expected to have a holistic understanding of software systems and the ability to work across the stack. The role includes building software products and crafting product experiences with a scientific method mindset. The position is in-person in the New York City office.

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Design, build, and optimize the core native runtime that powers LM Studio and the C++ libraries powering the app and APIs. Work across the runtime, LLM engines, llama.cpp/MLX integrations, build infrastructure, and on-device AI software. Focus on system and library integration by wiring the C++ runtime to GPU backends, vendor SDKs, and operating-system services to support user-facing applications. Implement and harden system-level code including threading, memory, files, IPC, and scheduling. Integrate platform acceleration paths such as Metal, CUDA, and Vulkan across macOS, Windows, and Linux. Profile, debug, and tune execution paths for local AI to be fast and dependable, and maintain well-architected software. Contribute to the C++ runtime powering LM Studio, extend LLM engine integrations, and build platform-aware performance features for desktop operating systems. Implement resilient IPC, resource management, and scheduling logic to support concurrent model execution. Improve build, packaging, and release infrastructure for native components. Collaborate with the team to deliver cohesive and recognizable user experiences.

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As the Director of Engineering for Infrastructure at Zapier, you will lead multiple multidisciplinary teams responsible for building, supporting, and evolving Zapier's core services, platforms, and infrastructure. Your responsibilities include shaping platform engineering vision, scalability, accountability mechanisms, and organizational operations. You will be responsible for defining and driving the strategy and long-term roadmap for the organization in collaboration with your teams and leadership peers, understanding and articulating how your work enables product development velocity, and how reactive and Keep-The-Lights-On (KTLO) work will be reduced to increase proactive platform improvements. You will lead the AI transformation of platform engineering by re-architecting workflows, minimizing reactive work, implementing AI-powered tooling and automation, setting AI adoption pace, and building repeatable AI-enhanced systems. Additionally, you will unblock software delivery pain points, establish data-driven approaches to measure delivery velocity and quality, ensure the reliability and uptime of core infrastructure in partnership with service-owning teams, and lead the organization during major incidents when necessary. You are also accountable for team building, talent development, recruiting, mentoring, and sustaining a compelling work culture that supports growth, with clear growth paths for managers and ICs, ultimately owning output and outcomes for your teams and their systems.

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Support the development and maintenance of performance modeling tools and frameworks; assist in building models to evaluate system behavior across compute, memory, networking, and interconnect subsystems; help analyze distributed system scaling behavior and identify performance bottlenecks; run simulations and analytical models to support architecture and infrastructure decisions; partner with senior engineers to evaluate design tradeoffs across hardware and system components; interpret modeling outputs and help translate findings into clear recommendations; validate models using benchmarking data and real system performance measurements; improve modeling workflows, documentation, and usability for broader team adoption; collaborate cross-functionally with hardware, infrastructure, and architecture teams; and continuously build technical depth across AI infrastructure, system architecture, and performance analysis.

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Develop and maintain performance modeling tools and frameworks. Build models to evaluate system behavior across compute, memory, and interconnect subsystems as well as distributed system scaling and bottlenecks. Run simulations and analytical models to support architectural tradeoff analysis. Collaborate with performance modeling lead and system architects to answer forward-looking design questions. Analyze and interpret modeling outputs, translating results into actionable insights. Validate models against real system measurements and workload behavior. Contribute to improving modeling fidelity, usability, and scalability.