(This role is US based remote and all candidates must be able to work in the US now and in the future without restriction)
What we believe
Technology is no longer just an enabler of business strategy. It is the business strategy. And with AI reshaping how companies operate, compete and grow — while rapidly accelerating the pace of change — the stakes have never been higher.
Our role
We’re focused on helping private equity investors and portfolio company leaders drive value creation through technology. In a world where investors deploy trillions annually into software and tech-enabled businesses, we're the team that makes sure the underlying technology actually delivers.
Our approach
The expert judgment of our experienced practitioners is complemented by proven frameworks, tech-enabled solutions, and objective data to help organizations navigate critical technology decisions across diligence, transformation, growth, cybersecurity, AI, and operational execution.
What we value
Everything we do is grounded in five core values: Service. Curiosity. Credibility. Commitment. Creativity.
If you're energized by solving complex technology challenges and helping others succeed in critical moments, you'll fit right in.
Job RoleWe’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.
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
Key Responsibilities
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
Requirements
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
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
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
