AI Automation Engineer

Later•Vancouver, BC

1d•$130,000 - $180,000•Hybrid

About The Position

Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments. Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch. By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.com. We're hiring an AI Automation Engineer to embed across teams at Later, Operations, Finance, Marketing, Customer Success, and beyond, and replace manual, repetitive work with durable AI-powered tooling. This role sits at the intersection of engineer, internal consultant, and coach: you'll build the automations that move the needle today and level up non-technical colleagues to build the next wave themselves. This is a high-leverage role for someone who gets energy from solving real business problems, ships pragmatic v1s in days rather than months, and knows when a scrappy prototype needs to be hardened into something the whole org can rely on. You'll work across Later's lines of business (Later Influence, Later Social, and Mavely), with regular exposure to senior leadership and broad visibility into how the company actually runs.

Requirements

A track record of building and shipping automations, internal tools, or AI-powered workflows that delivered measurable business impact.
Strong fluency with modern AI tooling (LLMs, agents, RAG, prompt design) and comfort wiring together APIs, scripts, and no-code/low-code platforms.
A business-first mindset: you start with the problem and the user, not the technology, and can sharpen a non-technical partner's problem definition through the right questions.
Resourcefulness and a bias to action: you ship a working v1 in days, not months, and iterate from there.
An instinct for scale, knowing when something is good enough as a one-off versus when it needs to be built to serve the whole org.
Strong written and verbal communication skills, with the ability to explain technical tradeoffs to non-technical audiences and coach others to build their own solutions.
Comfort with data: pulling it, cleaning it, and reasoning about it.

Nice To Haves

Experience in an internal-tools, RevOps, BizOps, or 'applied AI' function is a plus.
A background in supporting or training non-technical builders (citizen developers, ops teams running their own tooling) is a bonus.

Responsibilities

Embed with non-technical stakeholders across Operations, Finance, Marketing, Customer Success, and other functions to understand their workflows in depth.
Surface the highest-impact automation opportunities and translate fuzzy, ambiguous problems into clear technical solutions.
Leave every stakeholder conversation with a sharper problem definition than you walked in with.
Build automations and internal tools using AI-native platforms, scripting, APIs, and whatever else gets the job done, pragmatism over purity.
Ship working v1s quickly so stakeholders can validate value before investment scales.
Wire together LLMs, agents, RAG patterns, and prompt design with existing systems to deliver real workflow outcomes.
Take working prototypes and harden them with the reliability, observability, error handling, and reusability needed for production use.
Define when a one-off solution is good enough as-is versus when it needs to be promoted into something the whole org can rely on.
Build the monitoring and documentation that lets automations run unattended and recover gracefully when they don't.
Coach and advise non-technical colleagues who are building their own automations: review their work, unblock them, and level up their skills over time.
Run lightweight enablement (working sessions, office hours, shared documentation) that grows the citizen-developer community at Later.
Communicate technical tradeoffs clearly to non-technical audiences and teach others to fish.
Identify patterns across teams and build shared components, templates, and frameworks that compound the org's automation leverage over time.
Create reusable building blocks (prompt libraries, integration patterns, automation templates) that turn individual wins into org-wide capability.
Partner with Engineering and IT on the platform decisions that make this leverage durable.