Company Overview
Siena is the first intelligence layer for customer experience, creating an operating system of AI agents that learn, remember, and act across every customer touchpoint—from support conversations to shopping experiences to voice and social media interactions.
Role Summary
As a Technical Support Engineer, you own the space between our customers and Engineering. You manage Tier 2+ escalations end-to-end: diagnose, resolve, and close, pulling in engineers only when a product bug or infrastructure issue is involved. You collaborate directly with e-commerce brands running Siena’s AI agents and with Engineering on matters that require their involvement.
Key Responsibilities
* Full ownership of Tier 2+ escalations, diagnosing and resolving without engineering involvement unless necessary.
* Debug API integrations (auth failures, webhook issues, data sync gaps, rate limiting, intermittent failures, provider limits).
* Diagnose and resolve customer‑side JavaScript issues (widget behavior, event tracking, data passing, script conflicts, cross‑browser bugs).
* Triage AI and chatbot behavior—wrong answers, workflow misconfiguration, hallucinations, Custom Action misfires—tracing issue source.
* Run diagnostic SQL for data discrepancies, missing records, and report mismatches.
* When the queue is quiet, handle Tier 1 tickets with the Support team to identify recurring patterns.
* After each Tier 2+ resolution, determine if it can move back to Tier 1 with a runbook or training.
* Build & maintain internal documentation for common failure modes.
* Scope and drive systemic improvements such as auto‑routing, observability access, and tools to automate repetitive tasks.
* Surface patterns, recurring bugs, and product gaps to Engineering systematically.
* Own complex technical issues directly with customers, translating problems into clear explanations.
* Escalate to Engineering only when a code fix, infrastructure access, or schema migration is truly required.
* Support customers integrating Siena’s AI agents across Shopify, Gorgias, Zendesk, Kustomer, Gladly, and custom environments.
* Troubleshoot LLM‑based agent behavior, integration edge cases, and platform failures.
* Use AI tools actively in diagnostic workflow; remain fluent in AI products.
Technical Skills
* 1+ year of technical support at a SaaS company, owning hard threads end‑to‑end.
* Proficiency debugging REST APIs, webhooks, auth flows, JSON, and HTTP protocols.
* Fluent in customer‑side JavaScript: devtools, event handlers, script conflicts, timing issues.
* Ability to write diagnostic SQL to validate data and trace ingestion paths.
* Comfort with production system logs, monitoring tools, and system debugging.
AI/LLM Expertise (preferred)
* Experience with LLMs, prompt engineering, or AI model integration.
* Ability to triage AI agent behavior and distinguish prompt/config issues from model‑level problems.
* Familiarity with LLM failure modes: hallucinations, retrieval gaps, prompt sensitivity, workflow misconfiguration, Custom Action misfires.
* Experience with AI APIs (OpenAI, Anthropic, etc.).
Customer‑Facing Skills
* Strong written and verbal English, explaining complex issues to non‑technical stakeholders.
* Builds trust quickly, giving customers confidence their problem is in capable hands.
* Empathy and urgency coexist naturally.
* Proficient in independent remote work with strong asynchronous communication.
Startup Mindset
* Comfortable with ambiguity; creates structure where none exists.
* Leverages past wins to prevent future escalations, not just fix tickets.
* Coached or trained non‑technical teammates; documented processes used widely.
* Available to cover North America time zones.
Success Criteria
90 Days
* Engineering no longer pulled into support escalations; Tier 2+ resolution owned by the engineer.
* Established baseline for Tier 2+ resolution time.
* Mapped most common technical failure patterns across customers.
* At least one Tier 2 pattern moved back to Tier 1 with an active runbook.
* Built partnership with Support Manager to prioritize next systematization.
6 Months
* Resolution time measurably improves from the 90‑day baseline.
* Improved CSAT on technical issues.
* Engineering fully protected from support interruptions.
* Systematic feedback loop to Engineering running—patterns, bugs, product gaps surfaced proactively.
* Support team resolves more independently; internal documentation covers common failure modes.
* Shipped at least one systemic improvement project (e.g., auto‑triage, observability access).
Nice to Have
* Supported AI or LLM‑based products with knowledge of hallucinations, prompt sensitivity, retrieval‑grounded responses.
* Background in e‑commerce or DTC, understanding of catalog, orders, fulfillment, returns.
* Familiarity with modern observability tooling: Honeycomb, Grafana, Splunk, Sentry, Kibana.
* Built internal tooling or automation to improve support workflows (triage bot, runbook generator, custom dashboard).
* Fluent with Claude Code or equivalent AI coding tools.
Benefits
* Meaningful impact—shape product and company directly.
* Globally distributed team at the bleeding edge of CX and AI.
* Competitive salary with equity or stock grant opportunities.
* Learning budget; continuous growth support.
* Build something new at an impactful stage of the company.
* AI fluency—make AI second nature.
* Opportunity to tackle unsolved customer‑experience problems.
Siena provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.
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