AI Platform Engineer Jobs

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Port and validate key inference and training workloads on new platforms/SKUs as they arrive, driving correctness, performance, and stability to an internal readiness bar. Build a suite of benchmarks and stress tests that capture real end-to-end behavior of workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking, storage, thermals, and other relevant parts. Conduct deep-dive performance analysis on distributed training and inference focusing on collective performance and tuning, overlap of compute/communication, kernel-level bottlenecks, memory bandwidth, and scheduling effects. Create repeatable test harnesses that run in continuous integration and lab environments producing actionable outputs such as pass/fail, performance scores, and regression detection. Partner with systems and fleet bring-up engineers to ensure the platform is stable, performant, operationally usable, and scalable through containerization, Kubernetes integration, telemetry hooks, and failure triage loops. Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal reproductions, and prioritized issue lists.

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As a Software Engineer on the Developer Experience team, you will be responsible for creating frameworks and systems that maximize the velocity and efficiency of every engineer at Harvey. You will develop and scale a world-class developer platform to accelerate Harvey's growth, boosting velocity and stability through robust CI/CD systems, effective test frameworks, and reliable development environments. You will build load testing and benchmarking infrastructure essential for evaluating and optimizing the performance of AI-native applications. You will pioneer the future of software development and site reliability engineering by integrating AI agents across the software development, deployment, and maintenance lifecycle. You will collaborate with Backend Platform teams to embed testability, reliability, and observability into the platform, ensuring services built on the foundation are robust, easy to test, and maintain. You will work closely with engineering teams to gather feedback, evangelize best practices, and make the “paved road” a reality—empowering every Harvey engineer to move fast with confidence. You will also set the strategic direction and roadmap for scaling developer experience as Harvey expands, contribute strategically to team decision-making, and provide strong technical leadership and mentorship to uphold a high bar for engineering excellence across the team.

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Build the core systems that power agents including the Agent SDK such as the orchestration engine, runtime, and primitives that define how agents reason, take actions, and interact with users and systems. Design the agentic loop to build agents that are steerable, verifiable, conversational, and adaptive. Improve retrieval and grounding systems to ensure agents provide accurate and trustworthy responses by effectively retrieving and using knowledge. Build evaluation systems by designing frameworks that allow measurement and improvement of agent quality over time.

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The Senior Platform Engineer will own and advance the platform infrastructure stack that supports running autonomous agents safely in deployed customer environments. Responsibilities include managing sandboxing, isolation, monitoring, and safe operation of agent workloads at scale, covering execution environments, security boundaries, automated quality assurance, evaluation harnesses, and feedback loops to improve agent reliability. The role also involves working on core infrastructure such as Kubernetes, multi-account AWS, CI/CD, deployment strategies, observability including traces, metrics, logs, alerting, SLOs, disaster recovery, and cost management. Additionally, the engineer will handle security posture tasks including access controls, secrets management, network security, image scanning, dependency auditing, and compliance work like SOC2 as required by customers. All infrastructure will be defined, provisioned, and evolved through infrastructure-as-code.

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As an Engineering Leader at Ema, you will build and lead a high-performance engineering organization by recruiting, hiring, and developing senior engineers across multiple sub-teams including cloud infrastructure, data platform, ML operations, and developer experience. You will establish engineering standards, a code review culture, on-call expectations, and promote a bias-toward-shipping mentality balanced with production rigor. You will coach and grow senior and staff engineers into technical leaders and manage engineering managers as the organization scales. Your responsibilities include setting the 6–18 month platform roadmap in partnership with engineering teams, making critical architectural decisions such as build versus buy and migration strategies, and driving cross-functional alignment with product, ML/AI research, and go-to-market teams. You will own production health for all platform services, including incident response, postmortems, SLO tracking, and capacity planning. Additionally, you will establish and refine engineering practices to maintain fast shipping without compromising reliability, and participate in executive-level reviews related to infrastructure spend, system health, and engineering velocity.

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Create functional black box tests for large codebases in various source languages, create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms, monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards, and leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead response for production issues in mission-critical environments ensuring rapid resolution and prevention, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.

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The Head of Internal Tools Engineering is responsible for owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation, treating internal technology as a product. They make strategic build-vs-buy decisions, map current and next-state process flows, and lead systems transformation for internal teams. They architect and maintain the full engineering lifecycle of internal platforms, build seamless API-first ecosystems integrating various internal systems, ensure system reliability and operational resilience, and design scalable, secure architectures using cloud-native principles and microservices. They lead AI strategy by integrating AI and LLMs into internal workflows and deploying intelligent automation tools. They reduce cognitive load for internal users by providing standardized workflows and self-service capabilities, measure platform success by adoption, satisfaction, and productivity impact, and build, lead, and mentor a high-performing engineering team. They cultivate a collaborative culture, provide technical mentorship, foster psychological safety, partner cross-functionally with leadership across departments, and align internal platform investments with company strategy while demonstrating measurable ROI.

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The role involves architecting, building, and scaling the internal technology ecosystem to accelerate workforce productivity, eliminate operational friction, and provide a compounding infrastructure advantage by treating internal tools with product rigor and user-centricity. Responsibilities include owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation; making strategic build-vs-buy decisions; mapping current and next-state process flows and leading systems transformation. The role requires architecting and maintaining the full engineering lifecycle of internal platforms, building API-first ecosystems integrating with various business systems, owning system reliability and operational resilience, and designing scalable, secure cloud-native architectures. The role leads AI adoption and automation integration into internal workflows, including deploying intelligent automation tools, evaluating AI-assisted troubleshooting, and driving continuous experimentation with prototypes. The person will reduce cognitive load for internal users by providing golden paths and standardized workflows, ensuring frictionless onboarding, and measuring platform success via adoption rates, user satisfaction, DORA metrics, and productivity impact. Team leadership duties include building, leading, and mentoring engineers and managers, fostering a collaborative culture rooted in ownership, speed, craftsmanship, and psychological safety. The role partners cross-functionally with various company leadership teams to translate business needs into a unified technical vision, aligning internal platform investments with company strategy and demonstrating measurable ROI.

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The Senior Engineer on the internal tools team is responsible for building and maintaining internal platforms and tools used by various departments such as People, Finance, Ops, Sales, and Engineering. The role involves owning features end-to-end, including requirements gathering, architecture, implementation, testing, deployment, and monitoring. The engineer is expected to write clean, well-tested, production-grade code and build API-first integrations to connect multiple business systems like HRIS, CRM, finance platforms, and developer tools. Responsibilities include designing for reliability, performance, and scalability, eliminating data silos by creating clean data pipelines, and owning services in production with monitoring, alerting, incident response, and post-mortems. The role also involves building AI/LLM-powered features to automate internal workflows, moving prototypes to production, and staying updated on emerging AI technologies. Collaboration includes working directly with business stakeholders to translate pain points into technical solutions, mentoring junior engineers, conducting code and design reviews, influencing technical direction, proposing architectural improvements, and driving best practices across the team.