Senior AI Automation Engineer

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.

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.

Partnership & Discovery

• 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.

Building & Shipping

• 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.

Scaling & Reliability

• 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.

Enablement & Coaching

• 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.

Platform Thinking

• 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.

What success looks like:

• Within your first 6-12 months, success in this role will look like:
• A portfolio of automations in production with documented business impact (hours saved, error rates reduced, cycle times shortened) across at least three different teams.
• A measurable lift in the number of non-technical colleagues confidently building their own automations, supported by templates, documentation, and coaching you've put in place.
• A reusable library of components, patterns, and templates that other builders, technical and non-technical, can pick up and apply.
• Recognition as the go-to partner across Operations, Finance, Marketing, and Customer Success for 'can this be automated?' and 'is this worth automating?' questions.
• A clear point of view on Later's automation roadmap: what the next high-leverage opportunities are, and where the platform investments need to land.

What you bring:

• 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.
• 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.
• Comfort with data: pulling it, cleaning it, and reasoning about it.