Customer Success Engineer

Adaptive ML•New York, NY

13d

About The Position

Adaptive ML is a frontier AI startup building a Reinforcement Learning Operations (RLOps) platform that enables enterprises to specialise and deploy LLMs into production with measurable impact. We provide the core infrastructure to tune, evaluate, and serve specialised models at scale — pioneering task-specific LLM development and running production-ready workflows that serve millions of requests while optimising for both cost and performance across distributed systems. Our tightly-knit team was previously involved in the creation of state-of-the-art open-access large language models. We raised a $20M seed led by Index Ventures and ICONIQ in early 2024, and we’re already live in production with customers including Manulife, AT&T, and Deloitte, across travel and financial services — with much more to be announced soon. We’re looking for a Customer Success Engineer to be the technical backbone of our customer relationships — from the first technical conversation through to long-term production success. Based in New York City, you’ll operate across the full customer lifecycle: deep enough technically to win enterprise deals in pre-sales, and commercially minded enough to grow and retain the accounts you help land. The best people in this kind of role run rigorous discovery, design architectures that solve real business problems, and earn the kind of trust that makes customers come back for every major decision. You won’t just hand off after a deal closes. You’ll stay embedded: owning deployment success, driving adoption, and becoming the trusted technical advisor your customers rely on in production. The role suits someone who has operated at the intersection of Solutions Engineering and Customer Success before, or a strong Solutions Architect ready to own the full arc of customer value.

Requirements

3–6+ years in a customer-facing technical role — Solutions Engineer, Solutions Architect, Customer Success Engineer, or Technical Account Manager — ideally in B2B SaaS, cloud, or infrastructure.
Proven ability to operate across both pre-sales and post-sales: you’re as comfortable running a technical architecture review for a VP of Engineering as you are debugging a production incident with a DevOps team.
Track record with enterprise customers in complex technical environments — multi-stakeholder deals, long sales cycles, and durable post-sale technical relationships.
Demonstrable outcomes: successful deployments, adoption growth, expansion revenue, or strong renewal rates. You own the result, not just the activity.
Strong infrastructure instincts: you can confidently size GPU and storage requirements, reason about TCO trade-offs, and produce architecture diagrams that a CTO would trust.
Skilled at architecture design — you can whiteboard a solution live, document it clearly, and defend design decisions with technical rigour.
Hands-on with Kubernetes (k8s): you can investigate deployment issues, read and edit Helm charts, and navigate distributed systems problems in production environments.
Python proficiency — enough to build proof-of-concepts, write integration scripts, and benchmark model performance against real customer workloads.
Comfortable working alongside DevOps and SRE teams; you understand their tooling, constraints, and language.
You ask sharp discovery questions, design clean solutions, and earn trust through genuine expertise rather than commercial polish.
Strong communicator across audiences — you can simplify a complex distributed architecture for a CFO and go deep on inference latency with a staff ML engineer in the same afternoon.
You own outcomes. In fast-moving, ambiguous environments you figure out what matters and act — you don’t wait for a brief.
Genuine curiosity about generative AI and LLMs. The technology you’re selling and supporting moves fast; intellectual interest in the space makes a real difference.
Based in New York City, comfortable in the NA time zone for customer-facing coverage.

Nice To Haves

Experience at a fast-growth or early-stage company is a strong plus — you know what it takes to build things from scratch under pressure.
Familiarity with ML infrastructure concepts: model serving, LLM fine-tuning, inference optimisation, and the operational realities of running models at scale is a strong plus.

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).