August Law

August Law

Growth Marketing Manager

New York City

Sponsorship not specifiedDetected 150 days ago
JavaScriptPythonSQLData EngineeringData ScienceLLMsAgentic AISEOSEMHubSpotGrowth MarketingOutbound SalesResearch

About the role

  • You'll use LLMs, no-code and low-code tools, APIs, and scrappy prototypes to unlock new growth channels and scale what works.
  • You'll be embedded within the growth function, working across paid, lifecycle, outbound, and product-led growth.
  • Your toolkit will be distinct: GPTs, automation workflows, custom scripts, internal agents, and fast MVPs built to learn, not to impress.

Responsibilities

  • Own our GTM stack: Connect and optimize tools across marketing and sales to power outbound, inbound, and PLG motion
  • Build AI-powered workflows: Prototype and launch GPTs for ad copy, agents for segmentation, auto-generated landing pages, and smart automation across channels
  • Ship growth experiments fast: Build and launch MVPs using Retool, Bubble, Zapier, Vercel, or custom scripts without relying on Engineering
  • Automate repetitive work: Campaign QA, performance reporting, lead enrichment, data pipelines-if it's manual, make it automatic
  • Build and scale performance channels: Paid search, retargeting, SEO, and new experiments we haven't tried yet. Obsess over ROI and repeatable systems
  • Support PLG motion: Build landing pages, improve site speed, optimize conversion funnels, and run keyword research
  • Run outbound campaigns: Build and optimize systems that convert, from segmentation to sequencing
  • Collaborate cross-functionally: Work closely with design, product, and data science to test and refine ideas that unlock new growth levers
  • Team-building events
  • Relocation support to NYC (as needed)

Requirements

  • Hands-on experience with LLMs, automation, and experimentation.

Nice to have

  • AI-first approach: Hands-on experience with LLMs, automation, and experimentation.

Skills

  • Share tools, playbooks, and internal agents that help marketing and GTM teams move faster
  • Comfortable with Zapier/Make/Tray/N8N, Python, JavaScript, SQL, APIs, webhooks, and no-code tools like Retool or Framer
  • Prior experience in B2B SaaS teams
  • Built PLG funnels or self-serve acquisition from scratch
  • SEO and conversion optimization expertise
  • Comfort working with structured and unstructured data-cleaning, transforming, and piping it into useful workflows
  • Shipped AI-powered marketing tools, internal agents, or growth infrastructure

Benefits

  • Medical insurance coverage
  • Equinox or Chelsea Piers gym membership
  • In-office perks: lunch, dinner, snacks, drinks, and more
  • Uncapped Upside: Competitive base + commission, early equity ownership.
  • Bonus if you've built custom GPTs or internal agents

Company info

  • Today, legal work is done the way it has been done for a century - manually, by humans, in six-minute increments.
  • Every contract drafted from scratch.
  • Every memo researched line by line.
  • Every matter running through layers of review that someone is paying for at hundreds of dollars an hour.
  • That is about to change.
  • We believe that in five years, more than 80% of legal work will pass through a model before it ever reaches a human.
  • The implication is not faster lawyers - it is a fundamentally different way that legal services get bought and procured.
  • Our mission is to make high-quality legal work accessible and affordable for everyone.
  • We are going after the services spend, not the software spend - helping law firms restructure their delivery model around AI, and helping in-house teams do the same.
  • One engine of agents and workflows underneath every matter we touch.
  • We are a lean, in-person based in New York City, backed by leading investors and growing fast.
  • Every role at August is a chance to remake an industry while the window is open.
  • Shape not just your role but the company.
  • Scale your career as we scale the company.

This listing is sourced directly from August Law's careers page and normalized into a canonical job model.