AI Software Engineer Jobs

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if there is a better, faster way to solve problems. Work with full-stack proficiency across system boundaries, from frontend UX to LLM agents, database schema, and event infrastructures. Leverage AI tools to handle the boilerplate work so focus can be on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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The Senior Software Engineer for the Agentic AI Platform will design and implement solutions addressing challenges such as distributed systems, integration, and security, and robustly implement advanced intelligence features developed by the AI research team. They will work with the team to understand Archie’s capability roadmap and break down capabilities into technical development. Responsibilities include turning capability prototypes and proofs of concept from the AI research team into robust, scalable implementations, diagnosing and solving technical problems identified by the team or users, developing and acting on automated platform tests including software testing, agentic AI evaluations, and infrastructure, and improving Archie’s scalability and robustness through system architecture design and implementation. Ownership of production-grade software is expected, with potential growth into technical leadership for specialty areas as Archie becomes more sophisticated.

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As a Full Stack Software Engineer at Replicant, you will design and deliver technology that powers natural, human-like conversations at scale to help companies reduce wait times, improve customer satisfaction, and empower representatives to focus on complex problems. You will build rich user experiences and backend services that enable customers to design, launch, and monitor AI-powered conversations. Responsibilities include building new features for Replicant's core AI voice and chat products handling millions of daily conversations, shipping full stack end-to-end features quickly, integrating automatic speech recognition, text to speech, and conversational AI model improvements into products, refactoring, optimizing, and debugging production systems balancing latency, cost, and user experience, participating in regular on-call rotations monitoring live systems, continuously improving systems based on performance metrics and customer feedback, shaping a culture emphasizing knowledge sharing and mentorship across distributed systems and enterprise-scale AI design, and participating in team and company-wide office events with travel required.

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Ship production-grade backend and frontend features for core member and provider flows using React, TypeScript, APIs, and data layers, ensuring high polish and reliability. Own features end-to-end, including specification, building, testing, deployment, monitoring, and handling complex state, permissions, and edge cases. Build and maintain robust system hygiene, including instrumentation, dashboards and alerts, CI/CD pipelines, code reviews, and production debugging. Design, implement, and maintain AI-powered workflows comprising tool/function calling, structured outputs, Retrieval-Augmented Generation (RAG), evals, tracing, observability, prompt versioning, and guardrails. Build and operate workflow and agent flows using orchestration patterns similar to Temporal, Dagster, or Airflow, managing retries, idempotency, asynchronous job queues, and failure handling. Collaborate closely with cross-functional partners to deliver reliable, scalable, and user-centric healthcare products.

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Design, develop, and maintain web applications and backend services that integrate ML-powered features. Collaborate closely with Machine Learning Engineers and Product Managers to understand ML system requirements and translate them into robust software solutions. Build reliable, scalable, and low-latency services that support ML inference, data workflows, and AI-driven user experiences. Use LLMs to build scalable and reliable AI agents. Own the full software development lifecycle: design, implementation, testing, deployment, monitoring, and maintenance. Ensure high standards for code quality, testing, observability, and operational excellence. Troubleshoot production issues and participate in on-call or support rotations when needed. Mentor junior engineers and contribute to technical best practices across teams. Act as a strong cross-functional partner between product, engineering, and ML teams.

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Own the evaluation stack by building online and offline evaluation pipelines that measure agent quality across ephemeral, voluminous MELT data, code, and unstructured documents, and set metrics defining the experience. Define quality at scale by designing evaluations that capture trajectory quality in production incidents spanning hundreds of services with ephemeral, high-volume, and approximative ground truth, ensuring metrics predict real outcomes. Build platform abstractions for agents by designing core agent architectures and extending internal frameworks such as sub-agents, MCPs, and middleware to enable confident iteration and faster shipping with product, platform, and research teams. Productionize these systems by owning latency, observability, and uptime.

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The Evaluation Engineer will own the technical foundation of the auto-evaluation systems by building a comprehensive system that runs fast, is easy to use, and supports quickly building new evaluations. Responsibilities include improving the speed of the basic evals infrastructure with minimal latency, designing interfaces suitable for ML engineers, product managers, and customers, and ensuring the system architecture allows team members to easily add examples and run evaluations. The role also involves ensuring evaluations are accurate and reliable by encoding knowledge about how pharma customers make decisions, providing appropriate statistical tests, and confidence intervals for trustworthy results. Additionally, the engineer is expected to spend most time on the core eval platform, collaborate with the evals team on specific evals, mentor an evals engineering intern, and learn how users interact with the evaluation system to improve it.

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Own and lead the technical direction of Harvey in Word, the flagship Office Add-in product, alongside other Microsoft Office integrations. Build the AI-native editing experience, including agentic document rewrites, real-time redlining, and playbook automation for lawyers. Design and implement sophisticated integrations with Microsoft Word using the OfficeJS API, involving document manipulation, playbook reviews, and AI-assisted drafting. Architect scalable, maintainable solutions that address constraints and quirks of the Office Add-in environment across platforms including Windows, Mac, and Web. Build polished, high-performance interfaces with crisp user interactions and resilient error handling. Collaborate with product, design, and backend teams to shape APIs and user experience for AI-powered features like streaming results and tool-calling workflows. Manage Office Add-in requirement sets, versioning, and cross-platform compatibility to ensure broad and reliable support. Contribute to Harvey in Outlook and help develop the next generation of agentic AI interfaces across the Microsoft suite. Mentor engineers, drive technical decisions, and improve quality and developer experience.