GTM AI Engineering

Applied Compute•San Francisco, CA

22h•Onsite

About The Position

As a GTM AI Engineer at Applied Compute, you’ll build the software, agents, and data infrastructure that power the company’s go-to-market engine. These systems help identify promising accounts, reach the right people, and turn information from across the business into actionable insights. This is a software engineering role dedicated to internal GTM systems, involving the design, coding, deployment, and improvement of tools the team uses every day. You’ll own projects from problem definition through production, with responsibility for their architecture, reliability, and business impact. You’ll work closely with the GTM team to understand workflows, prioritize what to build, and improve systems as the company grows.

Requirements

Prior professional experience as a software engineer or machine learning engineer building and maintaining production systems on an engineering team.
Strong programming skills in Python or TypeScript, with experience working with APIs, databases, and integrations.
Hands-on experience building with LLMs, including tool use, agent workflows, and evaluations.
Comfort working with SQL and messy data, including reconciling information across multiple sources.
Strong engineering judgment and the ability to ship quickly while keeping systems understandable, testable, and maintainable.
Ownership and product sense, including the ability to turn an ambiguous business problem into a useful tool, work directly with its users, and improve it based on evidence.

Nice To Haves

Experience building internal tools, data pipelines, or automation used by a sales, marketing, or growth team.
Familiarity with CRM systems, enrichment providers, and sales engagement tools.
Experience designing experiments and measuring how software changes affect business outcomes.

Responsibilities

Build agents and applications for GTM workflows, including account research, prospect discovery, outreach personalization, inbound qualification, and pipeline reporting.
Connect the CRM, communication tools, meeting transcripts, and external data sources; keep account and contact information accurate, current, and accessible.
Identify manual bottlenecks and build reusable software that reduces repetitive work and helps the team act on relevant information.
Develop evaluations, review workflows, and feedback loops to improve research accuracy, generated content, and agent behavior.
Maintain a shared codebase with code review, automated testing, monitoring, documentation, and reliable deployments; debug failures and improve systems as usage grows.
Instrument workflows to measure effects on qualified pipeline, conversion, and time saved, and use results to guide experiments and engineering priorities.