Customer Success Engineer

🕒 March 10

🗽 New York – Remote

🗽 New York – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧑‍🔧 Technical Customer Success Manager

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Adaptive ML

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

Artificial Intelligence • SaaS • Enterprise

Adaptive ML is a company that specializes in adaptive engine technology, allowing businesses to tune and deploy open models using reinforcement learning for high-performance AI applications. They focus on enhancing generative AI by enabling fast inference, feedback collection, safety guardrails, automated A/B testing, and private cloud deployment using their Adaptive Engine. The company aims to optimize AI models to directly drive business outcomes while keeping data secure within the client's cloud. Adaptive ML is backed by significant investment and supports continuous monitoring and improvement of AI models to ensure quality and performance on specific use cases. They offer solutions that require minimal RL knowledge, making it accessible for companies to implement advanced AI capabilities. Their technology emphasizes privacy, control, and optimization of AI models for enterprise uses.

📋 Description

• 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).

🎯 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. • 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. • 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. • 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. • Comfortable working alongside DevOps and SRE teams; you understand their tooling, constraints, and language.

🏖️ Benefits

• Comprehensive medical (health, dental, and vision) insurance. • 401(k) plan with 4% matching. • Unlimited PTO — we strongly encourage at least 5 weeks each year. • Mental health, wellness, and personal development stipends. • Visa sponsorship available if required.