Software Engineer (backend), Gtm Tooling

Cockroach Labs Cockroach Labs · Data AI · United States · Remote · Sales

Backend Software Engineer to build internal knowledge and intelligence infrastructure using agentic execution and AI technologies. The role involves feature development, performance optimization, customer focus, enablement, and DevOps tooling for a new system called GTM OS. Requires experience with AI-powered systems, data pipelines for AI, and integrating AI capabilities with evaluation and monitoring.

What you'd actually do

  1. Design, implement, and maintain new features and integrations that enhance the GTM OS, our new system for delivering actionable intelligence across the company. This work will include both traditional and agentic engineering approaches, leveraging the latest AI technologies to build impactful tools and workflows.
  2. Investigate and resolve complex performance and reliability issues within the GTM OS and its data pipelines, ensuring production-grade availability. Build observability and monitoring capabilities into the system.
  3. Partner directly with internal teams to understand how they spend their time and identify opportunities where we can better support them. In some cases, this may involve building entirely new features; in others, it may mean making iterative improvements to existing systems and workflows.
  4. Present and consult across Cockroach Labs to showcase the work we’ve built and help teams understand how to best leverage it. This includes presentations, detailed documentation, and workshops.
  5. Continuously improve and manage the infrastructure, tooling, and supporting systems required to operate and scale the GTM OS effectively.

Skills

Required

  • 5+ years of hands-on software engineering experience, ideally with distributed systems or complex infrastructure projects.
  • Proficiency in at least one programming language; Go is preferred, but not required.
  • Strong understanding of containerization (Docker) and orchestration (Kubernetes).
  • Working knowledge of CI/CD pipelines, version control (Git), and Infrastructure as Code (Terraform, Ansible, etc.).
  • Experience working with at least one major public cloud provider (AWS, GCP, or Azure).
  • Understanding of SQL and relational database fundamentals, including schema design and query optimization.
  • Proven ability to diagnose and resolve complex technical issues in production environments.
  • A track record of designing and building AI-powered systems, tools, or infrastructure that others use — not just for your own productivity.
  • Demonstrated ability to translate ambiguous problems into scalable, production-grade AI-enabled solutions.
  • Experience integrating models and AI capabilities into systems with evaluation, monitoring, and iteration built in.
  • Strong written and verbal communication skills, capable of tailoring information to both technical and non-technical audiences.
  • Experience designing and managing data pipelines for AI systems, including ETL for LLMs, vector databases, or feature stores.

Nice to have

  • Go is preferred
  • Previous experience with distributed databases or high-availability systems is a plus.
  • Enthusiasm for staying up-to-date with emerging technologies and incorporating them into new or existing solutions.
  • Openness to giving and receiving feedback, with a mindset of continuous improvement.
  • Willingness to adapt to changing priorities in a fast-paced, innovative environment.

What the JD emphasized

  • A track record of designing and building AI-powered systems, tools, or infrastructure that others use — not just for your own productivity.
  • Demonstrated ability to translate ambiguous problems into scalable, production-grade AI-enabled solutions.
  • Experience integrating models and AI capabilities into systems with evaluation, monitoring, and iteration built in.
  • Experience designing and managing data pipelines for AI systems, including ETL for LLMs, vector databases, or feature stores.

Other signals

  • building internal knowledge and intelligence infrastructure
  • leveraging the latest in agentic execution
  • designing and building AI-powered systems, tools, or infrastructure that others use
  • integrating models and AI capabilities into systems with evaluation, monitoring, and iteration built in