Back to List
Gooseworks Hiring Founding Growth Engineer to Build AI-Powered Go-to-Market Coworker Workspace in San Francisco
Industry NewsAI AgentsGTM StrategyStartup Hiring

Gooseworks Hiring Founding Growth Engineer to Build AI-Powered Go-to-Market Coworker Workspace in San Francisco

Gooseworks, a Y Combinator-backed startup from the W23 cohort, has announced an opening for a Founding Growth Engineer. Based in San Francisco, the company is developing a specialized workspace where organizations can deploy and manage teams of AI coworkers designed for Go-to-Market (GTM) operations. Founded by the team behind the LLM observability platform Athina AI, Gooseworks aims to automate complex growth tasks such as outbound sales, SEO, and influencer marketing. The role offers a competitive compensation package ranging from $140,000 to $200,000 with significant equity. The company’s core thesis suggests that GTM work is undergoing a transformation similar to software engineering, where the primary challenge has shifted from model capability to orchestration and context management within a dedicated agentic workspace.

Hacker News

Key Takeaways

  • Strategic Hiring: Gooseworks is seeking a Founding Growth Engineer with a salary range of $140K - $200K and 0.50% - 1.50% equity in San Francisco.
  • Product Vision: The company is building a workspace for "OpenClaw-style" AI coworkers, providing agents with their own computers, memory, and specialized tools to execute GTM tasks.
  • Proven Leadership: The founding team, including Shiv Sakhuja, previously developed Athina AI, an LLM observability platform utilized by industry leaders like Perplexity and You.com.
  • Dual Business Model: Gooseworks operates via a self-serve PLG workspace and a "Growth-as-a-service" model that feeds a continuous learning loop for product playbooks.
  • Technical Focus: The role prioritizes building and tuning AI-powered growth engines over traditional marketing, utilizing tools like Claude Code, Codex, and Hermes.

In-Depth Analysis

The Evolution of GTM via Agentic Workspaces

Gooseworks is positioning itself at the forefront of a major shift in how companies approach growth and Go-to-Market (GTM) strategies. According to the company’s thesis, GTM work is currently experiencing a transformation analogous to the revolution seen in software coding three years ago. The founders argue that the primary bottleneck in the industry is no longer the underlying AI models themselves, but rather the orchestration, context, and the specific environment in which these agents operate.

To address this, Gooseworks has developed a workspace where AI coworkers are not merely chatbots but functional entities. Each AI coworker is equipped with its own computer, memory, files, and communication channels. This infrastructure allows them to perform high-leverage growth work that was previously manual, including outbound operations, SEO/AEO (Answer Engine Optimization), Reddit growth, and partnership management. By providing a dedicated environment for these agents, Gooseworks aims to move beyond simple automation toward autonomous execution of complex marketing playbooks.

The Growth-as-a-Service Learning Loop

The business strategy employed by Gooseworks is divided into two distinct but complementary offerings. First is the core product: a self-serve, Product-Led Growth (PLG) workspace where companies can manage their AI teams. Second is a "Growth-as-a-service" offering, where the Gooseworks team configures bespoke growth engines for B2B startups.

This dual approach serves as a critical learning loop for the company. By operating bespoke engines for customers, the team identifies successful strategies and technical configurations in real-time. These successful "bespoke" interventions are then distilled into templatized playbooks within the self-serve product. This ensures that the autonomous agents available to all users are constantly updated with the most effective, battle-tested growth strategies. The target Ideal Customer Profile (ICP) for this ecosystem includes founders and growth operators who possess a deep understanding of growth mechanics but seek to execute their strategies ten times faster through AI orchestration.

Technical Pedigree and the Founding Team

The credibility of Gooseworks is anchored in its founding team's history. The team of three previously built Athina AI, a platform dedicated to LLM observability and evaluation. The fact that Athina AI was adopted by high-profile AI companies such as Perplexity and You.com suggests a deep technical understanding of how large language models behave in production environments.

This background is reflected in the requirements for the Founding Growth Engineer role. Unlike a traditional marketing or sales position, this is a "builder" role. The engineer is expected to be deeply immersed in technical environments like Claude Code, Codex, OpenClaw, and Hermes. The role is split between operational tuning of AI engines for customers and research and development aimed at turning manual successes into autonomous playbooks. This technical rigor indicates that Gooseworks views growth as an engineering challenge rather than a purely creative or administrative one.

Industry Impact

The emergence of Gooseworks signals a broader trend in the AI industry: the move from "AI as a tool" to "AI as a coworker." By focusing on the workspace and orchestration layer, Gooseworks is addressing the practical difficulties businesses face when trying to integrate AI into their daily operations. If successful, this model could significantly lower the barrier to entry for startups looking to scale their GTM efforts without hiring massive marketing teams.

Furthermore, the focus on "AEO" (Answer Engine Optimization) alongside traditional SEO highlights the shifting landscape of digital discovery. As more users turn to AI-driven search engines, the tools required to maintain visibility are changing. Gooseworks’ inclusion of these specialized skills in their AI coworkers suggests that the next generation of growth platforms will need to be as technically sophisticated as the AI models they aim to influence.

Frequently Asked Questions

Question: What is the primary difference between Gooseworks and a traditional marketing automation tool?

Gooseworks provides a full workspace for "AI coworkers" rather than just simple automation scripts. Each AI agent has its own computer, memory, and tools, allowing it to perform complex, multi-step tasks like managing partnerships or influencer marketing autonomously, rather than just sending scheduled emails.

Question: What technical skills are required for the Founding Growth Engineer role?

The role is designed for a "builder" who is obsessed with growth as a craft. Candidates should be proficient in using AI development tools such as Claude Code, Codex, OpenClaw, and Hermes. The work involves half building/tuning growth engines and half R&D to create autonomous playbooks.

Question: Does Gooseworks offer support for international candidates?

Yes, the job posting specifies that Gooseworks will sponsor visas for the Founding Growth Engineer position, which is based in San Francisco, CA.

Related News

Managing AI Coding Through Agent Evaluation: Lessons from Meituan’s 310,000-Line Code Refactoring Project
Industry News

Managing AI Coding Through Agent Evaluation: Lessons from Meituan’s 310,000-Line Code Refactoring Project

The Meituan technical team has introduced a novel approach to managing AI-driven software development by applying Agent evaluation logic to large-scale code refactoring. With AI now capable of generating over 90% of code, the team argues that the primary challenge has shifted from generation speed to the implementation of effective constraints. Without unified standards, AI risks amplifying technical chaos. By refactoring 310,000 lines of code, Meituan demonstrated a framework involving technical debt sorting, rule construction, a standardized Refactoring SOP, and a Pre-PR mechanism. This system transforms high-cost refactoring projects into continuous, daily iterative actions. The practice highlights the necessity of moving beyond simple code generation toward a structured management model that ensures long-term system maintainability in an AI-centric development environment.

Meituan LongCat Open Sources General 365: A New Benchmark Revealing the Reasoning Limits of Modern AI
Industry News

Meituan LongCat Open Sources General 365: A New Benchmark Revealing the Reasoning Limits of Modern AI

The Meituan LongCat team has officially released General 365, a new open-source benchmark designed to evaluate the reasoning capabilities of large language models (LLMs). In an initial assessment of 26 mainstream models, the results highlight a significant gap in current AI reasoning performance. Gemini 3 Pro, currently regarded as one of the most powerful models globally, achieved an accuracy rate of only 62.8%. Furthermore, the vast majority of the models tested failed to reach the 60% threshold, which is traditionally considered a passing grade. This release by Meituan's technical team sets a rigorous new standard for the industry, emphasizing that complex reasoning remains a formidable challenge even for the most advanced artificial intelligence systems.

Meituan BI Architecture Evolution: Leveraging Metric Platforms and Enhanced Computing for Data Consistency
Industry News

Meituan BI Architecture Evolution: Leveraging Metric Platforms and Enhanced Computing for Data Consistency

Meituan's Data Platform team has unveiled a new generation of Business Intelligence (BI) architecture centered on a unified Metric Platform. By developing two core capabilities—Automatic Semantics and Enhanced Computing—the team addresses critical challenges inherent in traditional BI systems. These challenges include inconsistent data definitions, often described as 'data caliber confusion,' and suboptimal query performance resulting from the proliferation of personalized datasets. This strategic shift aims to streamline data analysis workflows, ensuring that metrics remain consistent across the organization while maintaining high-performance data retrieval and processing capabilities.