Senior Software Engineer, Tooling

In the past few years, private equity investors have invested more than a trillion dollars in software and tech-enabled companies. And in many cases, the underlying tech is the greatest enabler to the business strategy. But has the approach to govern technology value creation caught up to the magnitude of the risk?

We believe a better way is possible – a more programmatic, proactive approach to actively manage technology throughout the investment lifecycle – and that’s what we do.

We know that technology can create truly transformative change, and its role in business is only growing. Crosslake is here to support the changemakers and help them buy, build and run better technology.

You could be a good fit for Crosslake if you see yourself reflected in our guiding values:

Service. We effect change by empowering others.

Curiosity. We believe great advice starts with deep understanding.

Credibility. Our expertise is earned and proven.

Commitment. It’s our privilege to serve clients in their critical moments.

Creativity. We are inspired by the constant pursuit of better.

Overview

We’re building small, highly capable engineering pods (2–3 engineers) that own problems end-to-end and move quickly from idea to production. This role is for engineers who are comfortable operating across the full stack and using modern AI-enabled workflows to accelerate delivery.

This role is part of our Tooling team, focused on rapidly building lightweight internal tools that improve how Crosslake teams work. These tools are typically narrow in scope and designed to be created, tested, and deployed quickly. The emphasis is on speed, usefulness, and iteration over long-term productization.

You’ll be expected to design, build, and ship software with minimal handoffs—working across frontend, backend, and infrastructure as needed. This role prioritizes speed and iteration, with a focus on delivering immediate value.

What you’ll do

• Design and build small, high-impact full-stack tools from concept to deployment
• Own the full SDLC: design, development, testing, deployment, and iteration
• Work within a small pod to deliver quickly with a high degree of autonomy
• Rapidly prototype and validate solutions with internal stakeholders
• Integrate with third-party tools, APIs, and internal systems
• Make pragmatic technical decisions appropriate for fast-moving tools
• Use AI tools to accelerate development, testing, and code quality
• Continuously refine, replace, or retire tools based on usage and feedback

What we’re looking for

• 7+ years of software engineering experience
• Strong full-stack capability (frontend, backend, and cloud infrastructure)
• Comfortable stepping into an architectural role when needed
• Experience deploying and operating applications in cloud environments (AWS, Azure, or GCP)
• Solid understanding of the full SDLC
• Deep experience with (i.e., daily usage of) AI coding tools

AI-Native Development

• Hands-on experience using AI-assisted development tools beyond basic code generation
• Ability to leverage AI across the workflow (e.g., prototyping, debugging, test generation, QA, code review, security analysis)
• Experience combining AI with automation/orchestration to streamline workflows and reduce manual effort
• Familiarity with modern AI-enabled development environments and practices

Preferred Experience

• Infrastructure as Code (e.g., Terraform)
• CI/CD and modern DevOps practices
• Experience with workflow automation/orchestration tools (e.g., n8n, Zapier, Temporal, Airflow)
• Experience integrating SaaS tools and APIs (Slack, Notion, Jira, etc.)
• Comfort building lightweight internal UIs (dashboards, admin panels, etc.)
• Experience with one or more of the following languages: Python, TypeScript, Golang, Rust
• Basic understanding of data engineering principles

How we work

• Small teams, high ownership, minimal handoffs
• Fast, iterative delivery over heavy process
• Pragmatic decision-making over over-engineering
• Engineers are expected to operate across the stack, not within silos
• Many tools are short-lived or heavily iterated—impact matters more than longevity

What success looks like

• You ramp quickly and start contributing within weeks
• You identify inefficiencies and ship tools to address them
• You deliver simple solutions that meaningfully improve team productivity
• You avoid overbuilding and focus on practical outcomes
• You effectively use AI tools to increase speed and output