Customer Success Engineer

Adaptive ML is a frontier AI startup focused on building a Reinforcement Learning Operations platform to deploy LLMs into production. They are seeking a Customer Success Engineer to manage customer relationships from pre-sales through long-term success, ensuring technical onboarding, support, and optimization of deployments.

Responsibilities:

• Lead customer-facing workload planning — understanding model usage patterns, expected throughput, and infrastructure constraints to scope solutions accurately from day one
• Own solution architecture in the sales cycle: infra selection, TCO calculation, and performance benchmarking tailored to each prospect’s environment and LLM workloads
• Design and deliver compelling technical demos and proof-of-concept implementations that map Adaptive ML’s capabilities directly to customer pain points and existing infrastructure
• Respond to technical evaluations, RFPs, and security reviews; go deep with engineering and data science counterparts on architecture decisions and integration requirements
• Partner with Account Executives to shape deal strategy, accelerate procurement timelines, and remove technical blockers standing between a prospect and a signed contract
• Own technical onboarding end-to-end — designing integration architectures, working directly with customer engineering teams, and driving time-to-first-value
• Support and continuously optimise live deployments: cost optimisation, performance tuning, and workload expansion across multi-geo and multi-team customer environments
• Be the escalation point for production issues — investigating and debugging problems spanning k8s deployments, Helm configurations, model serving infrastructure, and distributed systems
• Drive workload expansion proactively: surface new use cases, additional model workflows, and untapped product capabilities that create value across your account portfolio
• Conduct regular technical and business reviews with customer stakeholders, translating infrastructure metrics into business impact and building the case for renewal and growth
• Build reusable technical assets — reference architectures, integration guides, runbooks, and demo environments — that scale knowledge and accelerate future deals
• Act as the voice of the customer internally: channel field insights directly to Product and Engineering to shape the roadmap and prioritisation
• Contribute to infra sizing and workload planning discussions alongside Solutions and DevOps colleagues, with particular focus on the NA region (NYC/Toronto coverage)