AI Platform Engineer Jobs

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.

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Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.

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Build, scale, and uphold the technical backbone of a global AI product by personally building critical AI systems and maintaining core AI infrastructure. Design model training, evaluation, and deployment pipelines; debug and resolve production AI failures; review and merge critical PRs; define standards for model lifecycle and experimentation. Shape engineering culture, design organizational structure and hiring strategy, and align AI roadmap with business goals. Act as the final technical decision-maker, owning AI quality, reliability, and scalability end-to-end, balancing research ambition with real product delivery, and leading engineers by example through real systems and real code.

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Drive overall AI and results quality across all of Harvey's products, including Assistant, Harvey Word, Vault, Workflows & Agents, and new products. Establish offline and online evaluation processes and tools, and foster a culture of continuous iteration and experimentation. Build core AI quality building blocks reusable across different teams and surface areas. Own search and retrieval quality for AI applications and other use cases. Collaborate closely with product engineering to continuously improve AI and search product quality and capabilities. Work with platform engineering to scale AI capabilities.

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Define the technological and product vision, strategy, and overall plan for the Assistant team, covering product engineering, core platform, and AI components. Ensure robust and consistent execution across the Assistant teams, partnering closely with current managers and senior individual contributors. Work with cross-functional teams (Product, Design, Research, etc.) to establish and achieve the Assistant's objectives. Guide the technical evolution of the Assistant into a scalable, agentic platform that is easily integrated with and built upon by other Harvey products. Recruit, guide, and develop a top-tier applied AI and product engineering staff. Guarantee the team develops infrastructure and systems that support swift product creation, iteration, and AI experimentation.

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Drive overall AI & results quality across all of Harvey's products including Assistant, Harvey Word, Vault, Workflows & Agents, and new products. Establish evaluation processes and tools, create and evangelize reusable AI building blocks, and work closely with product teams to continuously improve AI output quality. Build out tools and processes for AI quality and establish a culture of scientific experimentation, rigorous evaluation, and iterative improvements. Own search and retrieval quality for AI applications and other use cases. Collaborate with product engineering to improve quality and capabilities of AI and search products. Work with platform engineering to scale capabilities.

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The responsibilities include reviewing AI-generated collateral to help shape product strategy and refine AI outputs in collaboration with the ML team, providing design verification for internal hardware projects (Thermodynamic ASIC Verification), setting up and evaluating EDA tools to ensure internal tool usability and effective deployment on shared computing resources, creating testbench environments, assertions, and coverage from design documents to support product development and coverage closure, curating and annotating datasets to associate specific parts of chip specifications with test cases, establishing rigorous quality criteria for verification data and implementing continuous refinement processes, implementing data augmentation methods and automated quality assurance checks for high-fidelity data for ML training, generating synthetic data using AI-based methods to supplement real datasets, collaborating with ML teams to ensure synthetic data effectively challenges verification models, building automated pipelines to annotate test data and link it to chip specifications, and automating document parsing for contextual tagging and traceability.

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Lead and develop a team of 4-6 engineers, providing technical direction, mentorship, and career growth opportunities. Work with the engineering team to make, evaluate, and advocate for architectural decisions that align with Zapier's vision and technical standards. Collaborate across engineering teams to evaluate and inform their technical decisions, creating alignment and removing blockers for the execution of the roadmap. Champion problem-solving by identifying technical challenges and improving engineering processes. Partner with key stakeholders to tackle user problems effectively. Communicate effectively to lead the team and keep leadership informed on the team’s progress through one-on-ones, team meetings, and leadership updates. Review code and provide thoughtful feedback to improve code quality and engineer growth. Contribute to code when needed to fix bugs, improve developer experience, or prototype concepts. Foster a culture of experimentation, rapid learning, and continuous improvement.

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Lead Zapier’s AI Orchestration Zone in partnership with Product and Design, co-owning one of the company’s most strategic initiatives to unify core AI capabilities, drive technical direction, and shape the application of AI across Zapier's products for differentiated customer experiences. Prototype and push the boundaries of applied AI by experimenting with new models, tools, and approaches to deliver impactful, production-ready AI features that extend Zapier’s automation capabilities. Build and mentor a world-class engineering organization by attracting, developing, and retaining exceptional talent while fostering a culture of technical excellence, curiosity, and velocity. Drive a culture of innovation and technical rigor by balancing exploration with execution to turn emerging AI capabilities into scalable, reliable features that create measurable customer impact. Foster cross-functional alignment by collaborating with Product, Design, and Go-to-Market partners to define the AI roadmap, ensure execution against company strategy, and translate bold ideas into tangible outcomes. Champion engineering excellence by guiding architectural decisions, evolving Zapier’s AI infrastructure, and upholding best practices in reliability, scalability, and agile development. Play a key role in shaping Zapier’s next chapter and defining what AI-powered automation looks like for millions of customers and how Zapier evolves into an intelligent orchestration platform.