Digital Customer Success Manager, AI & Automation
New
S
SamsaraCustomer success software
Remote - US; open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.Full-TimeManager
Salary$105,910 — $160,200 USD annual base salary
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Job Details
- Experience
- At least 1 year working directly with LLMs or AI agents in production; 3+ years of experience, preferably in a B2B SaaS environment.
- Required Skills
- PythonQA AutomationSalesforceRegression testingRESTful APIs
Requirements
- Hands-on experience building and maintaining production LLM-based agents or automations.
- At least 1 year working directly with LLMs or AI agents in production.
- Python proficiency or skill using AI to code.
- Track record connecting AI to external systems, including CRMs, APIs, customer data, and operational tools.
- Experience using product usage data, health signals, activation milestones, and usage floors to identify risk and opportunity.
- Experience acting on customer risk and opportunity before it surfaces in a renewal conversation.
- Comfort running experiments and drawing sound conclusions from data.
- A systems mindset for managing a high-volume book of business through repeatable playbooks and motions.
- 3+ years of experience, preferably in a B2B SaaS environment.
- Ideal candidates have experience with Anthropic Claude or similar LLM APIs.
- Ideal candidates have experience supporting NRR, retention, or expansion on a digital or long-tail book of 100+ accounts.
- Ideal candidates have experience building regression test suites or evaluation frameworks for non-deterministic AI systems.
Responsibilities
- Identify customer pain points and opportunities using customer-centric analysis.
- Build digital customer success playbooks and design agent architectures.
- Write prompts and integrate agents with Salesforce, the Samsara platform, customer success tools, and external systems.
- Build guardrails and maintainable Python code.
- Define success criteria and evaluation frameworks before implementation.
- Create automated test suites covering real-world scenarios, edge cases, and known failure modes.
- Own deployment, versioning, and rollback plans for agents.
- Monitor production accuracy, latency, failure rates, and customer outcomes, and detect drift.
- Continuously update agents as products, playbooks, and real-world data evolve.
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