Technical Customer Support Specialist

Alloy.ai•Washington, DC

3d•$65,000 - $80,000•Hybrid

About The Position

As a Customer Support Specialist at Alloy, you will be at the forefront of our customers' experience with our software. You will diagnose the root cause of complex data issues and build the durable fixes that stop them recurring. Your technical curiosity is pivotal to delivering unparalleled service and fostering long-term customer relationships. This role provides Tier 2 and Tier 3 support to our growing customer base. You will investigate user, data, configuration and pipeline requests, working through raw retailer files, product and location master data, extractor logic and ingestion behavior to establish what actually went wrong before you fix it, alongside our engineering, data operations and account management teams. You will also own the content that keeps those questions from coming back: help centre articles, onboarding material, and training for new users. We do not have a separate L&D function, so this sits with you. You will be closer than anyone to where the product confuses people, and we expect you to turn that into concrete usability feedback rather than absorbing it ticket by ticket. This role sits at an inflection point. Our AI assistant, Lens, now answers a growing share of the routine questions that once came to Support, and you will help train and improve the knowledge it draws on. Support is changing quickly and we would rather change with it than defend the old shape of the job. The most valuable person here will be someone who measures their success partly by the tickets that stop arriving, and who wants to help define what support looks like when an AI handles the first layer. If that sounds like a diminished version of support work, this is not the right role. If it sounds like the interesting part, we should talk.

Requirements

Bachelor's or associate's degree in a technical or related field or equivalent SaaS work experience.
Two or more years in a technical support, solutions or data role. We care much more about how you think than how long you have been doing it. If you are early in your career but can show us you learn fast and reason well, apply.
Demonstrated experience investigating and resolving complex data issues, and a track record of fixing causes rather than symptoms. Comfort working with structured data: reading and comparing large files, understanding data models, identifiers and how records match across systems.
Hands-on experience using AI tools (Claude, ChatGPT, Gemini, Copilot or similar) to accelerate technical investigation, with a clear sense of where they are reliable and where they are not.
Experience communicating technical knowledge to both technical and non-technical audiences, including explaining what caused an issue and how to avoid it.
Experience creating documentation or training content that people actually used — help articles, runbooks, onboarding guides, internal wikis or video walkthroughs. A formal enablement title is not required; evidence that you can explain something complicated clearly in writing is.
Comfort delivering training and onboarding directly to users, whether that is a live session, office hours or a recorded walkthrough, and interest in doing more of it.
Knowledge of customer service best practices and experience supporting and resolving software issues.
Effective time management including the ability to handle multiple customer inquiries simultaneously, organize, and prioritize.

Nice To Haves

Experience with RAG systems, AI knowledge bases or prompt/context engineering is a strong plus—this person will help train and improve our AI support.
Industry experience in consumer goods, retail or supply chain is a plus.

Responsibilities

Triage inbound tickets and resolve the majority of them.
Respond to customer queries in a timely and accurate way via Alloy's ticketing system.
Investigate complex data issues end to end—tracing discrepancies through raw retailer files, extractor logic, master data and ingestion behavior to establish the root cause before applying a fix.
Identify recurring issues and drive them to permanent resolution—proposing extractor changes, guardrails, monitoring or product fixes rather than repeating the same manual remediation.
Use AI tools to investigate faster—summarizing large files, comparing datasets, drafting queries and narrowing hypotheses—while retaining judgment about what the output is telling you.
Improve the knowledge base our AI support draws on: writing and maintaining help articles, closing gaps you see in Lens's answers, and feeding real ticket patterns back into its context so it deflects more over time.
Document the things customers keep needing: how people actually use Alloy to solve a given problem, the best practices worth recommending, and the retailer-specific context—what a given retailer reports, how their data behaves, what to expect from their feeds.
Start to think about the architecture of our knowledge, not just its volume: what belongs in a help article versus internal documentation versus Lens's context, how it stays current, and how someone actually finds it at the moment they need it.
Build and run enablement for our customers—onboarding material, training sessions and self-serve content—and measure whether it changes what arrives in the queue.
Proactively seek opportunities to improve internal processes & the wider customer experience.
Collaborate cross-functionally to maintain high levels of product knowledge and share actionable customer insights.