Customer Success Engineer

📋 Description

• • Adaptive ML is at the forefront of AI innovation, developing a groundbreaking Reinforcement Learning Operations (RLOps) platform designed to empower enterprises in specializing and deploying Large Language Models (LLMs) into production environments with tangible, measurable impact. Our platform provides the essential infrastructure for tuning, evaluating, and serving specialized models at an enterprise scale, pioneering the development of task-specific LLMs and facilitating production-ready workflows capable of handling millions of requests while meticulously optimizing for both cost and performance across complex distributed systems.
• • The company boasts a highly skilled and cohesive team, with prior experience in creating state-of-the-art open-access large language models. Demonstrating significant investor confidence, Adaptive ML secured a $20 million seed funding round led by Index Ventures and ICONIQ in early 2024. The platform is already actively deployed in production with prominent clients such as Manulife, AT&T, and Deloitte, serving diverse sectors including travel and financial services, with further customer announcements anticipated soon.
• • As a Customer Success Engineer, you will serve as the critical technical anchor for our customer relationships, guiding them from initial technical engagements through to sustained success in production. This role, based in our New York City office, encompasses the entire customer lifecycle. You will possess the technical depth required to secure enterprise deals during the pre-sales phase and the commercial acumen necessary to foster growth and retention within the accounts you help establish.
• • The ideal candidate for this position will excel at conducting rigorous discovery processes, architecting solutions that address complex business challenges, and cultivating the deep trust that encourages customers to engage for every significant decision. Your responsibilities extend beyond closing a deal; you will remain deeply integrated with the customer, owning deployment success, driving user adoption, and evolving into the indispensable technical advisor that customers rely on for their production operations.
• • This role is perfectly suited for individuals who have prior experience at the intersection of Solutions Engineering and Customer Success, or for accomplished Solutions Architects eager to manage the complete arc of customer value realization.
• • Pre-Sales Responsibilities:
• • Lead comprehensive customer-facing workload planning, meticulously understanding model usage patterns, anticipated throughput, and existing infrastructure constraints to ensure accurate solution scoping from the outset.
• • Take ownership of solution architecture throughout the sales cycle, including infrastructure selection, Total Cost of Ownership (TCO) calculations, and performance benchmarking, all tailored to each prospect’s unique environment and specific LLM workloads.
• • Design and deliver impactful technical demonstrations and proof-of-concept (POC) implementations that clearly articulate how Adaptive ML's capabilities directly address customer pain points and integrate seamlessly with their existing infrastructure.
• • Prepare and deliver responses to technical evaluations, Requests for Proposals (RFPs), and security reviews, collaborating closely with internal engineering and data science teams to define architecture decisions and integration requirements.
• • Partner strategically with Account Executives to refine deal strategies, expedite procurement processes, and proactively remove any technical obstacles hindering a prospect from signing a contract.
• • Post-Sales Responsibilities:
• • Manage the end-to-end technical onboarding process, which includes designing integration architectures, working directly with customer engineering teams, and driving rapid time-to-first-value.
• • Provide ongoing support and continuous optimization for live deployments, focusing on cost reduction, performance tuning, and workload expansion across multi-geographical and multi-team customer environments.
• • Serve as the primary escalation point for production issues, conducting thorough investigations and debugging of problems that may span Kubernetes (k8s) deployments, Helm configurations, model serving infrastructure, and complex distributed systems.
• • Proactively identify and drive workload expansion opportunities by surfacing new use cases, additional model workflows, and untapped product capabilities that can deliver enhanced value across your assigned customer portfolio.
• • Conduct regular technical and business reviews with key customer stakeholders, effectively translating infrastructure performance metrics into clear business impact and building a compelling case for contract renewal and future growth.
• • Internal & Cross-Functional Contributions:
• • Develop and maintain reusable technical assets, such as reference architectures, detailed integration guides, operational runbooks, and sophisticated demo environments, to facilitate knowledge sharing and accelerate future sales cycles.
• • Act as the internal advocate for the customer, channeling field insights and feedback directly to the Product and Engineering teams to influence roadmap development and prioritization.
• • Participate actively in infrastructure sizing and workload planning discussions alongside Solutions and DevOps colleagues, with a specific emphasis on supporting the North American region (covering NYC and Toronto).